Plant Seedlings Classification

Determine a seedling Species from an image

Tags

#Plant #Seedlings #Leaf #OpenCV2 #Matplotlib #Seaborn #LabelEncoder #PCA #t-SNE #EigenVector #CNN #TransferLearning #Xception #VGG #Keras #Sklearn #ClassifierComparison #XGB

• Author : Indiano
• September 2018

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Content

1. Introduction (5 min)
   • Objective
   • Description
   • Evaluation
   • Imports Libraries
2. Data Understanding (15 min)
   • Helper Functions
   • Load data
   • Basic Statistical summaries and visualisations
3. Data Preparation (25 min)
   • Sanitize Data
   • Categorize Class Labels
   • Advanced Statistical Summaries & Visualisations
   • PCA Visualization
   • t-SNE Visualization (2D & 3D)
   • Normalization
4. Modeling, Evaluation & Submission (25 min)
   • CNN
   • Transfer Learning using Xception, VGG, ImageNet
   • XGB using only countour features
5. Deployment (5 min)
   • Submit result to Kaggle leaderboard
6. Further Improvements
   • Some ideas for increasing the accuracy

Adopted from Cross Industry Standard Process for Data Mining (CRISP-DM)

1. Introduction

Based on Kaggle Plant Seedlings Classification

1.1 Objective

Classify an image of seedling into one the following 12 different seedling classes.

1. Black-grass
2. Charlock
3. Cleavers
4. Common Chickweed
5. Common wheat
6. Fat Hen
7. Loose Silky-bent
8. Maize
9. Scentless Mayweed
10. Shepherds Purse
11. Small-flowered Cranesbill
12. Sugar beet

1.2 Description

Can you differentiate a weed from a crop seedling?

The ability to do so effectively can mean better crop yields and better stewardship of the environment.

The Aarhus University Signal Processing group, in collaboration with University of Southern Denmark, has recently released a dataset containing images of approximately 960 unique plants belonging to 12 species at several growth stages.

We're hosting this dataset as a Kaggle competition in order to give it wider exposure, to give the community an opportunity to experiment with different image recognition techniques, as well to provide a place to cross-pollenate ideas.

Citation

A Public Image Database for Benchmark of Plant Seedling Classification Algorithms

1.3 Evaluation

Submissions are evaluated on MeanFScore, which at Kaggle is actually a micro-averaged F1-score.

Given positive/negative rates for each class k, the resulting score is computed this way:

$Precisionmicro=∑k∈CTPk∑k∈CTPk+FPk$

$Recallmicro=∑k∈CTPk∑k∈CTPk+FNk$

F1-score is the harmonic mean of precision and recall

$MeanFScore=F1micro= $\Frac{2PrecisionmicroRecallmicro}{Precisionmicro+Recallmicro}$

1.4 Imports

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

# Theano MKL Problem
import os
os.environ["MKL_THREADING_LAYER"] = "GNU"

# Formatting
import pprint as pretty
from tabulate import tabulate

# Memory usage
import ipython_memory_usage.ipython_memory_usage as imu

# System related libraries
import os
import importlib
from joblib import Parallel, delayed
from sklearn.externals import joblib
from time import time

# Handle table-like data and matrices
import pickle
import numpy as np
import pandas as pd

# Collections
from itertools import product, compress
from functools import reduce
from operator import itemgetter
from collections import defaultdict
from glob import glob

# Image
import imageio
import cv2

# Sklearn
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn import decomposition
from sklearn.neighbors import KernelDensity
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from skimage.transform import resize as imresize

# Modelling Algorithms
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression, Ridge, Lasso, RandomizedLasso
from sklearn.svm import SVC, LinearSVC
from xgboost import XGBClassifier

# Modelling Helpers
from sklearn.model_selection import train_test_split, StratifiedKFold

# Keras
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Input, Dense, Flatten, Activation, Dropout, Maximum, ZeroPadding2D
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate
from keras import regularizers
from keras.layers import BatchNormalization
from keras.optimizers import Adam, SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, TensorBoard
from keras.layers.advanced_activations import LeakyReLU
from keras.utils import to_categorical
from keras.applications import xception, vgg16

# Auto-ML
import autosklearn.classification
from tpot import TPOTClassifier

# Visualization
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec

from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from mpl_toolkits.axes_grid1 import ImageGrid
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation
import seaborn as sns

from IPython.display import display, HTML
from tqdm import tqdm

# Configure visualisations
plt.style.use('ggplot')
sns.set_style('white')
# pylab.rcParams[ 'figure.figsize' ] = 8 , 6
%matplotlib inline

# Monitoring memory usage in jupyter notebooks; mprof run test.py & mprof plot
# %memit
%load_ext memory_profiler

The memory_profiler extension is already loaded. To reload it, use:
  %reload_ext memory_profiler

from subprocess import check_output
print(check_output(["ls", "./"]).decode("utf8"))

checkpoint
checkpoint (copy)
data
logs
logs1536318716.4047165
save
seedlings
Seedlings.ipynb
visualization

%ls data/all/train

 Black-grass/        'Common wheat'/      'Scentless Mayweed'/
 Charlock/           'Fat Hen'/           'Shepherds Purse'/
 Cleavers/           'Loose Silky-bent'/  'Small-flowered Cranesbill'/
'Common Chickweed'/   Maize/              'Sugar beet'/

%memit

peak memory: 312.84 MiB, increment: 0.03 MiB

# Same as label_to_id_dict

CLASS = {
    'Black-grass': 0,
    'Charlock': 1,
    'Cleavers': 2,
    'Common Chickweed': 3,
    'Common wheat': 4,
    'Fat Hen': 5,
    'Loose Silky-bent': 6,
    'Maize': 7,
    'Scentless Mayweed': 8,
    'Shepherds Purse': 9,
    'Small-flowered Cranesbill': 10,
    'Sugar beet': 11
}

# Same as id_to_label_dict

INV_CLASS = {
    0: 'Black-grass',
    1: 'Charlock',
    2: 'Cleavers',
    3: 'Common Chickweed',
    4: 'Common wheat',
    5: 'Fat Hen',
    6: 'Loose Silky-bent',
    7: 'Maize',
    8: 'Scentless Mayweed',
    9: 'Shepherds Purse',
    10: 'Small-flowered Cranesbill',
    11: 'Sugar beet'
}

2. Data Understanding

# Data directory

root_dir = './data/all'
train_dir = os.path.join(root_dir, 'train')
test_dir = os.path.join(root_dir, 'test')

2.1 Helper Functions

2.1.1 Mask, Segment & Sharpen Functions

The create_mask_for_plant function returns an image mask matrix of image_height * image_width shape. The mask matrix contains only boolean values of 0 & 1 indicating seedlings background and foreground region.

At the end, we will do morphological close operation to keep the original shape of the foreground (1 blob on the mask image) but close the spurious small holes.

# Segment Mask & Sharpen for the plant


def create_mask_for_plant(image):

    # Convert to HSV image
    image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # Create mask
    sensitivity = 35
    lower_hsv = np.array([60 - sensitivity, 100, 50])
    upper_hsv = np.array([60 + sensitivity, 255, 255])

    mask = cv2.inRange(image_hsv, lower_hsv, upper_hsv)
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

    return mask


def segment_plant(image):
    mask = create_mask_for_plant(image)
    output = cv2.bitwise_and(image, image, mask=mask)
    return output


def sharpen_image(image):
    image_blurred = cv2.GaussianBlur(image, (0, 0), 3)
    image_sharp = cv2.addWeighted(image, 1.5, image_blurred, -0.5, 0)
    return image_sharp


# Mask, Segment & Sharpen an Image


def mark_segment_sharpen_image(img, img_size=(45, 45)):
    # Resize image
    img = cv2.resize(img.copy(), img_size, interpolation=cv2.INTER_AREA)

    image_mask = create_mask_for_plant(img)
    image_segmented = segment_plant(img)
    image_sharpened = sharpen_image(image_segmented)

    return img, image_mask, image_segmented, image_sharpened

2.1.2 Contour Calculation Functions

# Find contours and calculate the largest contour & total area


def find_contours(mask_image):
    return cv2.findContours(mask_image, cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)[-2]


def calculate_largest_contour_area(contours):
    if len(contours) == 0:
        return 0
    c = max(contours, key=cv2.contourArea)
    return cv2.contourArea(c)


def calculate_contours_area(contours, min_contour_area=250):
    area = 0
    for c in contours:
        c_area = cv2.contourArea(c)
        if c_area >= min_contour_area:
            area += c_area
    return area

2.1.3 Visualize Images Functions

# Plot the plant images on a grid according to each class label


def image_grid_plot_for_classes(labels=['Black-grass'], nb_cols=12):

    fig = plt.figure(1, figsize=(nb_cols, nb_cols))
    grid = ImageGrid(
        fig, 111, nrows_ncols=(len(labels), nb_cols), axes_pad=0.05)

    # Iterating over different class labels
    for i, label in enumerate(labels):
        for j in range(0, nb_cols):
            axs = grid[i * nb_cols + j]
            axs.imshow(cv2.resize(images_per_class[label][j], (150, 150)))
            axs.axis('off')

        # Class label text
        axs.text(170, 75, label, verticalalignment='center')

    plt.show()


# Plot the plant images on a grid according to each class label


def grid_plot_for_class(labels=['Black-grass'], nb_cols=12):

    nb_rows, nb_cols = len(labels), nb_cols

    fig, axs = plt.subplots(nb_rows, nb_cols, figsize=(12, 12))

    # Iterating over different class labels
    for i, label in enumerate(labels):
        for j in range(0, nb_cols):
            axs[i, j].imshow(
                cv2.resize(images_per_class[label][j], (150, 150)),
                aspect='auto')
            axs[i, j].axis('off')

        axs[i, j].text(170, 75, label, verticalalignment='center')

    plt.show()


# Plot the plant images according to each class label


def plot_for_class(label, nb_rows=3, nb_cols=3):

    fig, axs = plt.subplots(nb_rows, nb_cols, figsize=(12, 12))

    n = 0
    for i in range(0, nb_rows):
        for j in range(0, nb_cols):
            axs[i, j].xaxis.set_ticklabels([])
            axs[i, j].yaxis.set_ticklabels([])
            axs[i, j].imshow(images_per_class[label][n])
            n += 1


# visualize_scatter_with_images


def visualize_scatter_with_images(X_2d_data,
                                  images,
                                  figsize=(45, 45),
                                  image_zoom=1):
    fig, ax = plt.subplots(figsize=figsize)
    artists = []
    for xy, i in zip(X_2d_data, images):
        x0, y0 = xy
        img = OffsetImage(i, zoom=image_zoom)
        ab = AnnotationBbox(img, (x0, y0), xycoords='data', frameon=False)
        artists.append(ax.add_artist(ab))
    ax.update_datalim(X_2d_data)
    ax.autoscale()
    plt.show()


# visualize_scatter


def visualize_scatter(data_2d, label_ids, figsize=(20, 20)):
    plt.figure(figsize=figsize)
    plt.grid()

    nb_classes = len(np.unique(label_ids))

    for label_id in np.unique(label_ids):
        plt.scatter(
            data_2d[np.where(label_ids == label_id), 0],
            data_2d[np.where(label_ids == label_id), 1],
            marker='o',
            color=plt.cm.Set1(label_id / float(nb_classes)),
            linewidth='1',
            alpha=0.8,
            label=id_to_label_dict[label_id])
    plt.legend(loc='best')


# visualize_scatter with 3D animation


def visualize_scatter_3D(data_3d, label_ids):

    fig = plt.figure(figsize=(25, 25))
    ax = fig.add_subplot(111, projection='3d')
    plt.grid()

    nb_classes = len(np.unique(label_ids))

    for label_id in np.unique(label_ids):
        ax.scatter(
            data_3d[np.where(label_ids == label_id), 0],
            data_3d[np.where(label_ids == label_id), 1],
            data_3d[np.where(label_ids == label_id), 2],
            alpha=0.8,
            color=plt.cm.Set1(label_id / float(nb_classes)),
            marker='o',
            label=id_to_label_dict[label_id])

    ax.legend(loc='best')
    ax.view_init(25, 45)
    ax.set_xlim(-2.5, 2.5)
    ax.set_ylim(-2.5, 2.5)
    ax.set_zlim(-2.5, 2.5)

    # Create GIF
    anima = animation.FuncAnimation(
        fig,
        lambda frame_number: ax.view_init(30, 4 * frame_number),
        interval=175,
        frames=90)
    anima.save(
        os.path.join('./visualization', 'seedlings_3D.gif'),
        writer='imagemagick')

2.1.4 Plot the missclassified plant images on a grid & Confusion Matrix

# Plot the missclassified plant images on a grid


def grid_plot_for_class(missclassified, nb_cols=3):

    nb_rows = int(missclassified.shape[0] / nb_cols) + 1

    fig = plt.figure(figsize=(15, 15))

    # Iterating over different missclassified images
    for index, (_, image) in enumerate(missclassified.iterrows()):

        # Add a subplot. Either a 3-digit integer or three separate integers describing the position of the subplot.
        # If the three integers are R, C, and P in order, the subplot will take the Pth position on
        # a grid with R rows and C columns.
        axs = fig.add_subplot(nb_rows, nb_cols, index + 1)

        axs.text(
            0, -15, INV_CLASS[image['Prediction']], horizontalalignment='left')
        axs.imshow(cv2.resize(image['Image'], (150, 150)), aspect='auto')
        axs.text(
            75,
            -15,
            'True: {}'.format(INV_CLASS[image['True']]),
            horizontalalignment='left')
        axs.axis('off')

    plt.show()


# Plot the confusion matrix


def plot_confusion_matrix(cm,
                          target_names,
                          plt_name,
                          rootdir='./',
                          save_dir='save/',
                          title='Confusion matrix',
                          cmap=None,
                          normalize=False):
    """
        plot_confusion_matrix function prints & plots the confusion matrix.
        Normalization can be applied by setting `normalize=True`.
        :param cm:confusion matrix from sklearn.metrics.confusion_matrix
        :param target_names:classification classes list eg. [0, 1] ['high', 'medium', 'low']
        :param rootdir:str
        :param save_dir:str
        :param plt_name:str
        :param title:str
        :param cmap:color map list
        :param normalize:bool
        :return:
        """

    plt_name += '_ConfusionMatrix'
    if normalize:
        plt_name = '{}_Normalized'.format(plt_name)

    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(12, 10))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(
                j,
                i,
                "{:0.4f}".format(cm[i, j]),
                horizontalalignment="center",
                color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(
                j,
                i,
                "{:,}".format(cm[i, j]),
                horizontalalignment="center",
                color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\n\nAccuracy={:0.4f}; Misclassified={:0.4f}'.
               format(accuracy, misclass))

    print('\n\nSaving Confusion Matrices in the {} directory'.format(rootdir +
                                                                     save_dir))
    plt.savefig(
        rootdir + save_dir + '/{}.png'.format(plt_name),
        dpi=200,
        format='png',
        bbox_inches='tight')

    plt.show()
    plt.close()

2.2 Load Data

Let's read the seedlings images in BGR (Blue/Green/Red) OpenCV's default format. It won't affect the segmentation in-case if you'd like to use RGB format. Anyhow, HSV (Hue/Saturation/Value) color space will be used for the processing of images.

First Approach: Using Simple dictionary

# Flatten the plant images data after Segmentation & Masking


def load_seedlings(train_dir=os.path.join(root_dir, 'train'),
                   init_img_size=(150, 150),
                   final_img_size=(45, 45)):
    images = []
    labels = []
    images_per_class = defaultdict(list)

    for class_folder_name in os.listdir(train_dir):
        class_folder_path = os.path.join(train_dir, class_folder_name)

        for image_path in glob(os.path.join(class_folder_path, "*.png")):
            # Read an image
            image = cv2.imread(image_path, cv2.IMREAD_COLOR)

            # Stats for each seedlings class
            images_per_class[class_folder_name].append(image)

            # Resize image to default init_img_size pixels 150*150
            image = cv2.resize(
                image, init_img_size, interpolation=cv2.INTER_AREA)

            # Segementation
            image = segment_plant(image)

            # BGR2GRAY conversion
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

            # Resize image to default final_img_size pixels 45*45
            if (init_img_size != final_img_size):
                image = cv2.resize(
                    image, img_size, interpolation=cv2.INTER_AREA)

            image = image.flatten()

            images.append(image)
            labels.append(class_folder_name)

    images = np.array(images)
    labels = np.array(labels)

    return images, labels, images_per_class

# Load Images, Labels & images_per_class dict

try:
    print('Loading Images, Labels & images_per_class dict ...')
    images = joblib.load(os.path.join('./save/pickles', 'images.pickle'))
    labels = joblib.load(os.path.join('./save/pickles', 'labels.pickle'))
    images_per_class = joblib.load(
        os.path.join('./save/pickles', 'images_per_class.pickle'))
    print('Loading done.')
except Exception as e:
    print('Stacktrace', e)
    print('Images, Labels or images_per_class dict does not exist.')

if not (images.size or labels.size) or (not images_per_class):
    print('Running load_seedlings method ...')
    images, labels, images_per_class = load_seedlings()

    print('Dumping Images, Labels & images_per_class dict ...')
    # Dumping dicts
    joblib.dump(images, os.path.join('./save/pickles', 'images.pickle'))
    joblib.dump(labels, os.path.join('./save/pickles', 'labels.pickle'))
    joblib.dump(images_per_class,
                os.path.join('./save/pickles', 'images_per_class.pickle'))
    print('Images, Labels or images_per_class dict dumping done.')

Loading Images, Labels & images_per_class dict ...
Loading done.

Number of images per class

for key, value in images_per_class.items():
    print("{0} -> {1}".format(key, len(value)))

Charlock -> 390
Common Chickweed -> 611
Shepherds Purse -> 231
Small-flowered Cranesbill -> 496
Common wheat -> 221
Fat Hen -> 475
Cleavers -> 287
Sugar beet -> 385
Maize -> 221
Loose Silky-bent -> 654
Scentless Mayweed -> 516
Black-grass -> 263

Second Approach: Using Train & Test dictionary

# Resize all image to 51x51
def img_reshape(img):
    img = imresize(img, (51, 51, 3))
    return img


# get image tag
def img_label(path):
    return str(str(path.split('/')[-1]))


# get plant class on image
def img_class(path):
    return str(path.split('/')[-2])


# fill train and test dict
def fill_dict(paths, some_dict):
    text = ''
    if 'train' in paths[0]:
        text = 'Start fill train_dict'
    elif 'test' in paths[0]:
        text = 'Start fill test_dict'

    for p in tqdm(paths, ascii=True, ncols=85, desc=text):
        img = imageio.imread(p)
        img = img_reshape(img)
        some_dict['image'].append(img)
        some_dict['label'].append(img_label(p))
        if 'train' in paths[0]:
            some_dict['class'].append(img_class(p))

    return some_dict


# read image from dir. and fill train and test dict
def reader():
    file_ext = []
    train_path = []
    test_path = []

    for root, dirs, files in os.walk('./data/all'):
        if dirs != []:
            print('Root:\n' + str(root))
            print('Dirs:\n' + str(dirs))
        else:
            for f in files:
                ext = os.path.splitext(str(f))[1][1:]

                if ext not in file_ext:
                    file_ext.append(ext)

                if 'train' in root:
                    path = os.path.join(root, f)
                    train_path.append(path)
                elif 'test' in root:
                    path = os.path.join(root, f)
                    test_path.append(path)
    train_dict = {'image': [], 'label': [], 'class': []}
    test_dict = {'image': [], 'label': []}

    train_dict = fill_dict(train_path, train_dict)
    test_dict = fill_dict(test_path, test_dict)

    return train_dict, test_dict

2.3 Basic Statistical Summaries & Visualisations¶

To understand the data we are now going to consider some key facts about seedlings class and count.

# Dataframe containing seedlings class and count

images_per_class_df = pd.DataFrame({
    'Class': list(images_per_class.keys()),
    'Count': [len(value) for value in images_per_class.values()]
})
images_per_class_df.head()

	Class	Count
0	Charlock	390
1	Common Chickweed	611
2	Shepherds Purse	231
3	Small-flowered Cranesbill	496
4	Common wheat	221

# Plot seedlings by class and count

seaborn = sns.FacetGrid(images_per_class_df, size=5, aspect=2)
seaborn.map(sns.barplot, 'Class', 'Count', palette='deep')
seaborn.set_xticklabels(rotation=30)
seaborn.add_legend()

Plot an image grid of Small-flowered Cranesbill plant seedlings

plot_for_class("Small-flowered Cranesbill", nb_rows=10, nb_cols=10)

Plot an image grid of Maize plant seedlings

plot_for_class("Maize", nb_rows=10, nb_cols=10)

Plot an image grid of All seedling

image_grid_plot_for_classes(list(images_per_class.keys()), nb_cols=12)

grid_plot_for_class(list(images_per_class.keys()), nb_cols=12)

3. Data Preparation

After statistics analysis & visualizing the different aspects of images, we will sanitize or clean the training data for our model.

3.1 Sanitize Data

As we can see that each image has a background which makes contour of plant leaves obscure or unclear. Hence we will segregate background from foreground and hope it might help us achieve better accuracy.

For removing the background, we'll use the fact that all plant leaves are green and we can create a mask to remove background.

Masking, Segmenting & Sharpening Green

For creating mask, which will remove background, we need to convert BGR/RGB image to HSV. HSV is alternative of the BGR/RGB color model. In HSV, it is easier to represent a color range than in BGR/RGB color space.

Being a simple object detection problem, we will use the color of the object for background segregation. The HSV color-space is suitable for color detection because we can define a color with Hue and it's variations or spectrum using Saturation & Value. eg. Red, Darker Red, Lighter Red.

The following figure illustrates the HSV color space.

As hue varies from 0 to 1.0, the corresponding colors vary from red through yellow, green, cyan, blue, magenta, and back to red, so that there are actually red values both at 0 and 1.0. As saturation varies from 0 to 1.0, the corresponding colors (hues) vary from unsaturated (shades of gray) to fully saturated (no white component). As value, or brightness, varies from 0 to 1.0, the corresponding colors become increasingly brighter.

HSV to RGB Color Space

# Plot the plant images according to each class label


def plot_masked_image(images, nb_cols=4):

    # Generate figure, axes
    fig, axes = plt.subplots(
        nrows=len(images), ncols=nb_cols, figsize=(12, 12))

    # Iterating over different trials & parameters
    for index, image in enumerate(images):

        # Mask, Segment & Sharpen
        image_mask = create_mask_for_plant(image)
        image_segmented = segment_plant(image)
        image_sharpen = sharpen_image(image_segmented)

        # Show images
        axes[index, 0].imshow(image)
        axes[index, 1].imshow(image_mask)
        axes[index, 2].imshow(image_segmented)
        axes[index, 3].imshow(image_sharpen)

        # Set x, y tick labels
        axes[index, 0].xaxis.set_ticklabels([])
        axes[index, 0].yaxis.set_ticklabels([])
        axes[index, 1].xaxis.set_ticklabels([])
        axes[index, 1].yaxis.set_ticklabels([])
        axes[index, 2].xaxis.set_ticklabels([])
        axes[index, 2].yaxis.set_ticklabels([])
        axes[index, 3].xaxis.set_ticklabels([])
        axes[index, 3].yaxis.set_ticklabels([])

    plt.show()

plot_masked_image(images_per_class["Small-flowered Cranesbill"][97:101])

# Test image to see the changes

image = images_per_class["Small-flowered Cranesbill"][97]

image_mask = create_mask_for_plant(image)
image_segmented = segment_plant(image)
image_sharpened = sharpen_image(image_segmented)

# Segment Mask & Sharpen for the plant using mark_segment_sharpen_image method
# image, image_mask, image_segmented, image_sharpened = mark_segment_sharpen_image(image, img_size=(150, 150))

fig, axs = plt.subplots(1, 4, figsize=(20, 20))
axs[0].imshow(image)
axs[1].imshow(image_mask)
axs[2].imshow(image_segmented)
axs[3].imshow(image_sharpened)

<matplotlib.image.AxesImage at 0x7fd869bc4438>

3.2 Categorize Class Labels

Let's create a dictionary and reverse dictionary containing mapping from label to id & vice-versa.

First Approach: Using Simple dictionary with for loop

# Create a mapping from a flower class to an unique integer id & vice-versa

label_to_id_dict = {v: i for i, v in enumerate(np.unique(labels))}
id_to_label_dict = {v: k for k, v in label_to_id_dict.items()}

# Covert train images label into id
label_ids = np.array([label_to_id_dict[x] for x in labels])

Second Approach: Using LabelEncoder

# Plot of label types numbers
pd.Series(labels).value_counts().plot(
    kind='pie', title='Labels Distribution', figsize=(5, 5))
plt.show()

# Encode labels and create classes
le = LabelEncoder()
le.fit(labels)
labels_encoded = le.transform(labels)
print("\n\nClasses: ", le.classes_)

# Convert labels into categorical values
labels_onehot = to_categorical(labels_encoded)
print("\nNumber of One Hot encoded class labels: ", labels_onehot.shape[1])

Classes:  ['Black-grass' 'Charlock' 'Cleavers' 'Common Chickweed' 'Common wheat'
 'Fat Hen' 'Loose Silky-bent' 'Maize' 'Scentless Mayweed'
 'Shepherds Purse' 'Small-flowered Cranesbill' 'Sugar beet']

Number of One Hot encoded class labels:  12

3.3 Advanced Statistical Summaries & Visualisations¶

3.3.1 Contours Statistics

From the mask image, we can extract some features like the contour area and number of components etc. and understand how the area of a plant changes according to each class.

We can extract much more interesting information from contours. Please have a look below for further information.

Additional read: https://en.wikipedia.org/wiki/Image_moment

# Load images_per_class dictionary

images_per_class = joblib.load(
    os.path.join('./save/pickles', 'images_per_class.pickle'))

# Let's find contours and calculate the largest contour & total area

areas = []
larges_contour_areas = []
labels = []
nb_of_contours = []
images_height = []
images_width = []

for class_label in images_per_class.keys():
    for image in images_per_class[class_label]:

        # Mask for an image
        mask = create_mask_for_plant(image)

        # Find & calculate contour stats
        contours = find_contours(mask)
        area = calculate_contours_area(contours)
        largest_area = calculate_largest_contour_area(contours)
        height, width, channels = image.shape

        # Save the stats in the lists
        nb_of_contours.append(len(contours))
        areas.append(area)
        larges_contour_areas.append(largest_area)
        images_height.append(height)
        images_width.append(width)

        labels.append(class_label)

# Save the features in a dataframe

features_df = pd.DataFrame(data={
    'label': labels,
    'area': areas,
    'largest_contour_area': larges_contour_areas,
    'no_of_countours': nb_of_contours,
    'height': images_height,
    'width': images_width
})
features_df.to_hdf(
    os.path.join('./save/pickles', 'contours_features_df.hdf'),
    key='contours_features_df',
    mode='w')
features_df.groupby("label").describe()

	area								height		...	no_of_countours		width
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
label
Black-grass	263.0	41793.982890	159636.952451	0.0	1559.750	7204.00	23556.250	2097133.5	263.0	661.307985	...	22.00	195.0	263.0	668.638783	520.794148	73.0	290.00	586.0	906.00	2840.0
Charlock	390.0	91669.094872	150626.968468	3177.5	12795.125	39382.25	80786.125	1193936.5	390.0	431.976923	...	2.75	61.0	390.0	431.982051	287.746036	121.0	188.00	375.5	514.75	1582.0
Cleavers	287.0	25619.073171	24726.375631	567.5	8368.000	18717.50	34363.000	175187.0	287.0	310.292683	...	4.00	32.0	287.0	310.292683	151.918747	66.0	171.50	298.0	423.00	866.0
Common Chickweed	611.0	14935.878887	20172.494215	0.0	1917.500	5026.00	25140.000	131654.5	611.0	230.708674	...	2.00	24.0	611.0	230.708674	160.842547	54.0	107.50	156.0	376.50	718.0
Common wheat	221.0	6949.357466	8161.181143	0.0	1883.000	4044.50	8953.000	57880.0	221.0	440.167421	...	37.00	203.0	221.0	440.171946	303.226509	51.0	136.00	456.0	651.00	1432.0
Fat Hen	475.0	30623.629474	67760.225738	512.0	2804.250	6153.50	28209.000	561161.5	475.0	303.008421	...	5.00	88.0	475.0	303.008421	210.947401	55.0	162.00	234.0	369.00	1273.0
Loose Silky-bent	654.0	24618.366208	132431.879535	0.0	718.500	1795.50	8252.500	1930040.5	654.0	397.897554	...	12.00	305.0	654.0	399.596330	420.449270	71.0	136.00	197.0	535.75	3991.0
Maize	221.0	115311.341629	206882.627747	851.5	3885.500	52033.00	119356.000	1435536.5	221.0	483.239819	...	4.00	87.0	221.0	483.253394	417.085743	54.0	114.00	415.0	706.00	1900.0
Scentless Mayweed	516.0	15517.381783	27678.754205	0.0	1589.250	3314.25	14044.125	198328.5	516.0	226.003876	...	2.00	123.0	516.0	226.003876	211.790279	49.0	87.00	123.0	363.50	1227.0
Shepherds Purse	231.0	38187.586580	66440.811037	562.0	3303.500	7435.50	45623.250	415662.5	231.0	288.541126	...	2.00	49.0	231.0	288.541126	248.176509	63.0	117.50	169.0	400.00	1317.0
Small-flowered Cranesbill	496.0	38126.337702	48735.741208	678.5	7834.625	16201.50	42556.500	306815.5	496.0	342.316532	...	10.00	92.0	496.0	342.316532	194.215528	62.0	173.75	286.0	459.25	1006.0
Sugar beet	385.0	79003.370130	112091.057669	0.0	16029.000	40748.50	81174.000	815699.0	385.0	590.309091	...	4.00	19.0	385.0	591.477922	337.497855	49.0	373.00	551.0	744.00	1715.0

12 rows × 40 columns

features_df.head()

	area	height	label	largest_contour_area	no_of_countours	width
0	24198.5	360	Cleavers	12418.0	3	360
1	8176.0	149	Cleavers	8176.0	1	149
2	12717.0	225	Cleavers	12717.0	1	225
3	35139.0	404	Cleavers	21444.0	5	404
4	20688.0	315	Cleavers	9436.0	12	315

del images_per_class

3.3.2 PCA Visualization

Let's load the seedlings images & corresponding helper function.

Map Labels function

# Create a mapping from a flower class to an unique integer id & vice-versa

label_grey_to_id_dict = {v: i for i, v in enumerate(np.unique(labels_grey))}

# Covert train images label into id

labels_grey_ids = np.array([label_grey_to_id_dict[x] for x in labels_grey])

Load Seedlings function

# Return Grey the plant images data


def load_seedlings_grey(train_dir=os.path.join(root_dir, 'train'),
                        img_size=(150, 150)):
    images = []
    labels = []
    images_mask = []

    for class_folder_name in os.listdir(train_dir):
        class_folder_path = os.path.join(train_dir, class_folder_name)

        for image_path in glob(os.path.join(class_folder_path, "*.png")):
            # Read an image
            image = cv2.imread(image_path, cv2.IMREAD_COLOR)

            # Resize image to default init_img_size pixels 150*150
            image = cv2.resize(image, img_size, interpolation=cv2.INTER_AREA)

            _, image_mask, _, image = mark_segment_sharpen_image(
                image, img_size=(150, 150))

            # BGR2GRAY conversion
            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

            images.append(image)
            images_mask.append(image_mask)
            labels.append(class_folder_name)

    images = np.array(images)
    images_mask = np.array(images_mask)
    labels = np.array(labels)

    return images, images_mask, labels

images_grey = np.array([])
images_grey_mask = np.array([])
labels_grey = np.array([])

# Load Grey images & labels

try:
    print('Loading Grey Images, Images Mask & Labels dict ...')
    images_grey = joblib.load(
        os.path.join('./save/pickles', 'images-grey.pickle'))
    images_grey_mask = joblib.load(
        os.path.join('./save/pickles', 'images-grey-mask.pickle'))
    labels_grey = joblib.load(
        os.path.join('./save/pickles', 'labels-grey.pickle'))
    print('Loading done.')
except Exception as e:
    print('Stacktrace', e)
    print('Grey Images, Images Mask or Labels dict does not exist.')

if (not images_grey.size or not images_grey_mask.size or not labels_grey.size):
    img_size = (150, 150)
    print('Running Grey load_seedlings method with img_size {} ...'.format(
        img_size))
    images_grey, images_grey_mask, labels_grey = load_seedlings_grey(
        img_size=img_size)

    print('Dumping Grey Images, Images Mask & Labels dict ...')
    # Dumping dicts
    joblib.dump(images_grey,
                os.path.join('./save/pickles', 'images-grey.pickle'))
    joblib.dump(images_grey_mask,
                os.path.join('./save/pickles', 'images-grey-mask.pickle'))
    joblib.dump(labels_grey,
                os.path.join('./save/pickles', 'labels-grey.pickle'))
    print('Grey Images, Images Mask & Labels dict dumping done.')

print('Shape of images_grey: ', images_grey.shape)
print('Shape of images_grey_mask: ', images_grey_mask.shape)
print('Shape of labels_grey: ', labels_grey.shape)

Loading Grey Images & Labels dict ...
Stacktrace [Errno 2] No such file or directory: './save/pickles/images-grey-mask.pickle'
Grey Images & Labels dict does not exist.
Running Grey load_seedlings method with img_size (150, 150) ...
Dumping Grey Images & Labels dict ...
Grey Images & Labels dict dumping done.
Shape of images_grey:  (4750, 150, 150)
Shape of images_grey_mask:  (4750, 150, 150)
Shape of labels_grey:  (4750,)

# Sample image

plt.imshow(np.reshape(images_grey[2000], (150, 150)))

<matplotlib.image.AxesImage at 0x7f88dcd3f3c8>

3.3.2.1 Define GaussianModel or PCA

# %% define GaussianModel class


class GaussianModel:
    def __init__(self, X, numBasisFunctions=10, objectPixels=None):
        '''
        inputs: 
            X                    - numSamples x numDimentions matrix
            numBasisFunctions       - number of basis function to use
            objectPixels (optional) - an binnary mask image used for presentation
                                      will be used as Im[objectPixels] = dataSample
                                      must satisfy objectPixels.ravel().sum() = X.shape[1]
        '''

        self.numBasisFunctions = numBasisFunctions
        if objectPixels is None:
            self.objectPixels = np.ones((1, X.shape[1]), dtype=np.bool)
        else:
            self.objectPixels = objectPixels
        assert (self.objectPixels.ravel().sum() == X.shape[1])

        PCAModel = decomposition.PCA(n_components=numBasisFunctions,
                                     whiten=True)
        self.dataRepresentation = PCAModel.fit_transform(X)
        self.PCAModel = PCAModel

    def RepresentUsingModel(self, X):
        return self.PCAModel.transform(X)

    def ReconstructUsingModel(self, X_transformed):
        return self.PCAModel.inverse_transform(X_transformed)

    def InterpretUsingModel(self, X):
        return self.PCAModel.inverse_transform(self.PCAModel.transform(X))

    # shows the eigenvectors of the gaussian covariance matrix
    def ShowVarianceDirections(self, numDirectionsToShow=16):
        numDirectionsToShow = min(numDirectionsToShow, self.numBasisFunctions)

        numFigRows = 4
        numFigCols = 4
        numDirectionsPerFigure = numFigRows * numFigCols
        numFigures = int(
            np.ceil(float(numDirectionsToShow) / numDirectionsPerFigure))

        for figureInd in range(numFigures):
            plt.figure()
            for plotInd in range(numDirectionsPerFigure):
                eigVecInd = numDirectionsPerFigure * figureInd + plotInd
                if eigVecInd >= self.numBasisFunctions:
                    break
                deltaImage = np.zeros(np.shape(self.objectPixels))
                deltaImage[self.objectPixels] = self.PCAModel.components_[
                    eigVecInd, :].ravel()

                plt.subplot(numFigRows, numFigCols, plotInd + 1)
                if np.shape(self.objectPixels)[0] == 1:
                    plt.plot(deltaImage)
                else:
                    plt.imshow(deltaImage, cmap='jet')
                    plt.axis('off')
                titleStr = str(
                    100 *
                    self.PCAModel.explained_variance_ratio_[eigVecInd])[0:5]
                plt.title(titleStr + '% explained')
            plt.tight_layout()

    # shows several random model reconstructions
    def ShowReconstructions(self, X, numReconstructions=5):
        assert (np.shape(X)[1] == self.objectPixels.ravel().sum())
        numSamples = np.shape(X)[0]
        numReconstructions = min(numReconstructions, numSamples)

        originalImage = np.zeros(np.shape(self.objectPixels))
        reconstructedImage = np.zeros(np.shape(self.objectPixels))

        numReconstructionsPerFigure = min(5, numReconstructions)
        numFigures = int(
            np.ceil(float(numReconstructions) / numReconstructionsPerFigure))

        for figureInd in range(numFigures):
            plt.figure()
            for plotCol in range(numReconstructionsPerFigure):
                dataSampleInd = np.random.randint(numSamples)
                originalImage[self.objectPixels] = X[dataSampleInd, :].ravel()
                reconstructedImage[self.objectPixels] = \
                    self.InterpretUsingModel(np.reshape(
                        X[dataSampleInd, :], [1, -1])).ravel()
                diffImage = abs(originalImage - reconstructedImage)

                # original image
                plt.subplot(3, numReconstructionsPerFigure,
                            0 * numReconstructionsPerFigure + plotCol + 1)
                if np.shape(self.objectPixels)[0] == 1:
                    plt.plot(originalImage)
                    plt.title('original signal')
                else:
                    plt.imshow(originalImage, cmap='gray')
                    plt.title('original image')
                    plt.axis('off')

                # reconstred image
                plt.subplot(3, numReconstructionsPerFigure,
                            1 * numReconstructionsPerFigure + plotCol + 1)
                if np.shape(self.objectPixels)[0] == 1:
                    plt.plot(reconstructedImage)
                    plt.title('reconstructed signal')
                else:
                    plt.imshow(reconstructedImage, cmap='gray')
                    plt.title('reconstructed image')
                    plt.axis('off')

                # diff image
                plt.subplot(3, numReconstructionsPerFigure,
                            2 * numReconstructionsPerFigure + plotCol + 1)
                if np.shape(self.objectPixels)[0] == 1:
                    plt.plot(diffImage)
                    plt.title('abs difference signal')
                else:
                    plt.imshow(diffImage, cmap='gray')
                    plt.title('abs difference image')
                    plt.axis('off')
            plt.tight_layout()

    # shows distrbution along the variance directions and several images along that variance direction
    def ShowModelVariations(self, numVariations=5):

        showAsTraces = (np.shape(self.objectPixels)[0] == 1)
        numVariations = min(numVariations, self.numBasisFunctions)

        numVarsPerFigure = min(5, numVariations)
        numFigures = int(np.ceil(float(numVariations) / numVarsPerFigure))

        lowRepVec = np.percentile(self.dataRepresentation, 2, axis=0)
        medianRepVec = np.percentile(self.dataRepresentation, 50, axis=0)
        highRepVec = np.percentile(self.dataRepresentation, 98, axis=0)

        for figureInd in range(numFigures):
            plt.figure()
            for plotCol in range(numVarsPerFigure):
                eigVecInd = numVarsPerFigure * figureInd + plotCol
                if eigVecInd >= self.numBasisFunctions:
                    break

                # create the low and high precentile representation activation vectors
                currLowPrecentileRepVec = medianRepVec.copy()
                currLowPrecentileRepVec[eigVecInd] = lowRepVec[eigVecInd]
                currHighPrecentileRepVec = medianRepVec.copy()
                currHighPrecentileRepVec[eigVecInd] = highRepVec[eigVecInd]

                # create blank images
                deltaImage = np.zeros(np.shape(self.objectPixels))
                medianImage = np.zeros(np.shape(self.objectPixels))
                lowPrecentileImage = np.zeros(np.shape(self.objectPixels))
                highPrecentileImage = np.zeros(np.shape(self.objectPixels))

                # fill the object pixels with the relevant data
                deltaImage[self.objectPixels] = \
                    self.PCAModel.components_[eigVecInd, :].ravel()
                lowPrecentileImage[self.objectPixels] = \
                    self.ReconstructUsingModel(currLowPrecentileRepVec).ravel()
                medianImage[self.objectPixels] = \
                    self.ReconstructUsingModel(medianRepVec).ravel()
                highPrecentileImage[self.objectPixels] = \
                    self.ReconstructUsingModel(
                        currHighPrecentileRepVec).ravel()

                # calculate the Gaussian smoothed distribution of values along the eignevector direction
                sigmaOfKDE = 0.12
                pdfStart = min(
                    self.dataRepresentation[:, eigVecInd]) - 3 * sigmaOfKDE
                pdfStop = max(
                    self.dataRepresentation[:, eigVecInd]) + 3 * sigmaOfKDE
                xAxis = np.linspace(pdfStart, pdfStop, 200)
                PDF_Model = KernelDensity(
                    kernel='gaussian', bandwidth=sigmaOfKDE).fit(
                        self.dataRepresentation[:, eigVecInd].reshape(-1, 1))
                logPDF = PDF_Model.score_samples(xAxis.reshape(-1, 1))

                # show distribution of current component
                plt.subplot(5, numVarsPerFigure,
                            0 * numVarsPerFigure + plotCol + 1)
                plt.fill(xAxis, np.exp(logPDF), fc='b')
                percentExplainedString = str(
                    100 *
                    self.PCAModel.explained_variance_ratio_[eigVecInd])[0:5]
                plt.title(percentExplainedString + '% explained')

                # show variance direction (eigenvector)
                plt.subplot(5, numVarsPerFigure,
                            1 * numVarsPerFigure + plotCol + 1)
                if showAsTraces:
                    plt.plot(deltaImage)
                    plt.title('eigenvector ' + str(eigVecInd))
                else:
                    plt.imshow(deltaImage, cmap='jet')
                    plt.title('eigenvector ' + str(eigVecInd))
                    plt.axis('off')

                # show 2nd precentile image
                plt.subplot(5, numVarsPerFigure,
                            2 * numVarsPerFigure + plotCol + 1)
                if showAsTraces:
                    plt.plot(lowPrecentileImage)
                    plt.title('2nd precentile')
                else:
                    plt.imshow(lowPrecentileImage, cmap='gray')
                    plt.title('2nd precentile image')
                    plt.axis('off')

                # show median image
                plt.subplot(5, numVarsPerFigure,
                            3 * numVarsPerFigure + plotCol + 1)
                if showAsTraces:
                    plt.plot(medianImage)
                    plt.title('median signal')
                else:
                    plt.imshow(medianImage, cmap='gray')
                    plt.title('median Image')
                    plt.axis('off')

                # show 98th precentile image
                plt.subplot(5, numVarsPerFigure,
                            4 * numVarsPerFigure + plotCol + 1)
                if showAsTraces:
                    plt.plot(highPrecentileImage)
                    plt.title('98th precentile')
                else:
                    plt.imshow(highPrecentileImage, cmap='gray')
                    plt.title('98th precentile image')
                    plt.axis('off')
            plt.tight_layout()

    # shows distrbution along the variance directions and several images along that variance direction
    def ShowSingleComponentVariation(self, X, listOfComponents=[0, 1]):

        showAsTraces = (np.shape(self.objectPixels)[0] == 1)
        assert (all(
            [(x in range(self.numBasisFunctions)) for x in listOfComponents]))

        X_rep = self.RepresentUsingModel(X)

        percentilesToShow = [1, 20, 40, 60, 80, 99]
        numReadDataSamplePerPercentile = 4
        representationPercentiles = []
        for percentile in percentilesToShow:
            representationPercentiles.append(
                np.percentile(
                    self.dataRepresentation, percentile, axis=0))
        medianRepVec = np.percentile(self.dataRepresentation, 50, axis=0)

        for eigVecInd in listOfComponents:
            plt.figure()
            gs = gridspec.GridSpec(numReadDataSamplePerPercentile + 2,
                                   len(percentilesToShow))

            # calculate the Gaussian smoothed distribution of values along the eignevector direction
            sigmaOfKDE = 0.12
            pdfStart = min(self.dataRepresentation[:,
                                                   eigVecInd]) - 3 * sigmaOfKDE
            pdfStop = max(self.dataRepresentation[:,
                                                  eigVecInd]) + 3 * sigmaOfKDE
            xAxis = np.linspace(pdfStart, pdfStop, 200)
            PDF_Model = KernelDensity(
                kernel='gaussian', bandwidth=sigmaOfKDE).fit(
                    self.dataRepresentation[:, eigVecInd].reshape(-1, 1))
            logPDF = PDF_Model.score_samples(xAxis.reshape(-1, 1))
            percentileValuesToShow = \
                [representationPercentiles[x][eigVecInd]
                    for x in range(len(representationPercentiles))]
            percentilesToShowLogPDF = \
                PDF_Model.score_samples(
                    np.array(percentileValuesToShow).reshape(-1, 1))

            # show distribution of current component and red dots at the list of precentiles to show
            plt.subplot(gs[0, :])
            plt.fill(xAxis, np.exp(logPDF), fc='b')
            plt.scatter(
                percentileValuesToShow,
                np.exp(percentilesToShowLogPDF),
                c='r',
                s=40)
            plt.title(
                str(100 * self.PCAModel.explained_variance_ratio_[eigVecInd]) +
                '% explained')

            for plotCol, currPrecentile in enumerate(percentilesToShow):
                currPrecentileRepVec = medianRepVec.copy()
                currPrecentileRepVec[eigVecInd] = representationPercentiles[
                    plotCol][eigVecInd]

                currPrecentileImage = np.zeros(np.shape(self.objectPixels))
                currPrecentileImage[self.objectPixels] = \
                    self.ReconstructUsingModel(currPrecentileRepVec).ravel()

                # show the median image with current precentile as activation of the curr image
                plt.subplot(gs[1, plotCol])
                if showAsTraces:
                    plt.plot(currPrecentileImage)
                    plt.title('precentile: ' + str(percentilesToShow[plotCol])
                              + '%')
                else:
                    plt.imshow(currPrecentileImage, cmap='gray')
                    plt.title('precentile: ' + str(percentilesToShow[plotCol])
                              + '%')
                    plt.axis('off')

                # find the most suitible candidates in X for current precentile
                distFromPercentile = abs(X_rep[:, eigVecInd] -
                                         representationPercentiles[plotCol][
                                             eigVecInd])
                X_inds = np.argpartition(distFromPercentile,
                                         numReadDataSamplePerPercentile
                                         )[:numReadDataSamplePerPercentile]
                for k, X_ind in enumerate(X_inds):
                    currNearestPrecentileImage = np.zeros(
                        np.shape(self.objectPixels))
                    currNearestPrecentileImage[self.objectPixels] = X[
                        X_ind, :].ravel()

                    plt.subplot(gs[2 + k, plotCol])
                    if showAsTraces:
                        plt.plot(currNearestPrecentileImage)
                        plt.title('NN with closest percentile')
                    else:
                        plt.imshow(currNearestPrecentileImage, cmap='gray')
                        plt.title('NN with closest percentile')
                        plt.axis('off')
            plt.tight_layout()

    def ShowDataScatterPlotsWithTSNE(self,
                                     X=None,
                                     y=None,
                                     tSNE_perplexity=30.0,
                                     colorMap='Paired'):

        if X is None:
            X_rep = self.dataRepresentation
        else:
            X_rep = self.RepresentUsingModel(X)

        if y is None:
            y = np.ones(X_rep.shape[0])

        tSNE_PCAModel = TSNE(
            n_components=2, perplexity=tSNE_perplexity, random_state=0)
        X_rep_tSNE = tSNE_PCAModel.fit_transform(X_rep)
        (tSNE_xmin, tSNE_xmax) = (np.percentile(X_rep_tSNE[:, 0], 0.3),
                                  np.percentile(X_rep_tSNE[:, 0], 99.7))
        (tSNE_ymin, tSNE_ymax) = (np.percentile(X_rep_tSNE[:, 1], 0.3),
                                  np.percentile(X_rep_tSNE[:, 1], 99.7))

        plt.figure()
        plt.subplot(1, 2, 1)
        plt.scatter(
            X_rep[:, 0], X_rep[:, 1], c=y, cmap=colorMap, s=10, alpha=0.9)
        plt.title('PCA representation')
        plt.xlabel('PC1 coeff')
        plt.ylabel('PC2 coeff')
        plt.subplot(1, 2, 2)
        plt.scatter(
            X_rep_tSNE[:, 0],
            X_rep_tSNE[:, 1],
            c=y,
            cmap=colorMap,
            s=10,
            alpha=0.9)
        plt.xlim(tSNE_xmin, tSNE_xmax)
        plt.ylim(tSNE_ymin, tSNE_ymax)
        plt.title('t-SNE representation')
        plt.xlabel('t-SNE axis1')
        plt.ylabel('t-SNE axis2')

3.3.2.2 Train the GaussianModel

# Reshape images to be compatible with GaussianModel dimesions
# Number of images x Resize dimensions (150, 150)

images_grey_reshaped = images_grey.reshape(
    (np.shape(images_grey)[1] * np.shape(images_grey)[2]),
    np.shape(images_grey)[0]).T
print('Shape of images_grey_reshaped: ', images_grey_reshaped.shape)

# Create a boolean mask & train the model`
# Resize dimensions (150, 150)

bool_mask = np.ones((np.shape(images_grey)[1], np.shape(images_grey)[2])) == 1
print('Shape of bool_mask: ', bool_mask.shape)

# Fit the GaussianModel
leaf_pca = GaussianModel(
    images_grey_reshaped, numBasisFunctions=100, objectPixels=bool_mask)
print('GaussianModel fitted on grey images.')

Shape of images_grey_reshaped:  (4750, 22500)
Shape of bool_mask:  (150, 150)
GaussianModel fitted on grey images.

3.3.2.3 Principal Components Analysis

Now let's have a look at the main variance directions or vectors which also are known as Principal Components. The most varying directions explain the best discriminatory features for the Seedlings classification task in decreasing order. Each image can be thought as combination of different vectors in a high dimensional space.

Visualize the most Variance Directions

matplotlib.rcParams['font.size'] = 10
matplotlib.rcParams['figure.figsize'] = (12, 9)

leaf_pca.ShowVarianceDirections(numDirectionsToShow=16)

We can see some interesting shapes arising from the data especially in the first and second row.

But let's first look at some original images and how the low dimensional PCA model reconstructs them.

Visualize images & their Reconstructions

leaf_pca.ShowReconstructions(images_grey_reshaped, numReconstructions=10)

Looking at the absolute difference between original and reconstructed images, we notice the blurred leaf edges reconstruction using only 100 basis functions in GaussianModel.

Show Model Variations around the Mean Image

leaf_pca.ShowModelVariations(numVariations=5)

Details about above plots

• The upper most row contains the data distributions of each eigenvector (i.e. the histogram along that "direction")
• The second row contains what we already saw in a previous plot, what we called the variance directions.
• The forth row contains the median image of leafs. notice that this row is identical for all eigenvectors
• The third row holds the 2nd percentile images of each eigenvector. It's easier to think of this as the median image minus the eigenvector image multiplied by some constant.

The constant is chosen to show the varying degree of influence of this specific eigenvector on the "average" image, so we can visualize what type of variation this particular eigenvector tends to capture. The 2nd percentile will subtract a relatively large absolute value from the median image, showing us what images look like when this coefficient is highly negative. The 98th percentile would be just the opposite, showing us what images look like when this coefficient is at the upper end of the range. The 50th percentile would give us a "middle of the road" effect of this coefficient.

---

This plot helps us visualize what a direction in this high dimensional image space means.

For example:

• The first eigenvector (leftmost column), we can see that it controls the difference between large radius leafs and small radius leafs. i.e we can say that some of the variance along the change of leaf radius is explained by this component.
• The second eigenvector (second column from the left) controls the difference between an upright vetrically oriented leaf and a horizontally oriented leaf.

Eigen Vector I

matplotlib.rcParams['font.size'] = 6
matplotlib.rcParams['figure.figsize'] = (12, 8)

leaf_pca.ShowSingleComponentVariation(
    images_grey_reshaped, listOfComponents=[0])

Lets explain what we see:

• The first row shows the data distribution of the coefficients along this main variance direction. the red dots correspond to 1st, 20th, 40th, 60th, 80th and 99th percentiles of this distribution.
• The second row is like the columns were in the previous plot. for example, we can see here in this particular case a gradual increase in leaf size from left to right.
• The bottom 4 rows at each column hold real leaf images that have the first PCA coefficient be at the value of the corresponding percentile of that column. for example, the left most 4 bottom pictures are leafs with a PC1 coefficient to be approximately -1.6 and the right most 4 bottom pictures are leafs with a PC1 coefficient to be approximately 2.7

By examining the the leafs that have different coefficients we can see what this component coefficient represents. from the point of view of this particular component, the leaf images in the same column are very similar. we can therefore see what this particular feature "thinks" about similar leafs.

In this particular case we can see that it's about leaf size since we see a gradual increase in leaf size from left to right.

Eigen Vector II

matplotlib.rcParams['font.size'] = 6
matplotlib.rcParams['figure.figsize'] = (12, 8)

leaf_pca.ShowSingleComponentVariation(
    images_grey_reshaped, listOfComponents=[1])

Here we can see that the second principal component is about explaining the difference between vertical and horizontal leafs.

PCA & t-SNE Visualization

Let's visualize seedlings leaves using scatter plot as we have some understanding about the eigen vectors & their distributions.

1. Using the first two principal component coeffients.
2. Using 2D approximation with t-SNE.

leaf_pca.ShowDataScatterPlotsWithTSNE(
    images_grey_reshaped, labels_grey_ids, tSNE_perplexity=10.0)

Similar leaves should be clustered in nearby regions.

3.3.2.4 Fit models on different numbers of PCA

Now let's see how the classifiation accuracy changes across different models after varying number of Principal Components.

# Plot CV classification accuracy as function of num components used for 3 very different type of classifiers

matplotlib.rcParams['font.size'] = 12
matplotlib.rcParams['figure.figsize'] = (12, 8)

X_PCA = leaf_pca.RepresentUsingModel(images_grey_reshaped)

X_PCA_train = X_PCA
y_train = labels_grey_ids

numPCsToUse = [1, 2, 4, 8, 16, 32, 64]

logReg = LogisticRegression(C=10.0)
kNN = KNeighborsClassifier(n_neighbors=7)
RF = RandomForestClassifier(n_estimators=100)

logRegMeanAccuracy = []
kNN_MeanAccuracy = []
RF_MeanAccuracy = []
logRegAccuracyStd = []
kNN_AccuracyStd = []
RF_AccuracyStd = []

for numPCs in numPCsToUse:

    stratifiedCV = StratifiedKFold(n_splits=5, random_state=21)
    logRegAccuracy = []
    kNN_Accuracy = []
    RF_Accuracy = []

    for trainInds, validInds in stratifiedCV.split(X_PCA_train, y_train):
        X_train_cv = X_PCA_train[trainInds, :numPCs]
        X_valid_cv = X_PCA_train[validInds, :numPCs]

        y_train_cv = y_train[trainInds]
        y_valid_cv = y_train[validInds]

        logReg.fit(X_train_cv, y_train_cv)
        kNN.fit(X_train_cv, y_train_cv)
        RF.fit(X_train_cv, y_train_cv)

        logRegAccuracy.append(
            accuracy_score(y_valid_cv, logReg.predict(X_valid_cv)))
        kNN_Accuracy.append(
            accuracy_score(y_valid_cv, kNN.predict(X_valid_cv)))
        RF_Accuracy.append(accuracy_score(y_valid_cv, RF.predict(X_valid_cv)))

    logRegMeanAccuracy.append(np.array(logRegAccuracy).mean())
    logRegAccuracyStd.append(np.array(logRegAccuracy).std())

    kNN_MeanAccuracy.append(np.array(kNN_Accuracy).mean())
    kNN_AccuracyStd.append(np.array(kNN_Accuracy).std())

    RF_MeanAccuracy.append(np.array(RF_Accuracy).mean())
    RF_AccuracyStd.append(np.array(RF_Accuracy).std())

plt.figure()
plt.errorbar(x=numPCsToUse, y=logRegMeanAccuracy, yerr=logRegAccuracyStd)
plt.errorbar(x=numPCsToUse, y=kNN_MeanAccuracy, yerr=kNN_AccuracyStd)
plt.errorbar(x=numPCsToUse, y=RF_MeanAccuracy, yerr=RF_AccuracyStd)
plt.xlim(min(numPCsToUse) - 1, max(numPCsToUse) + 1)
plt.legend(
    ['Logistic Regression', 'k Nearest Neighbor', 'Random Forest'], loc=2)
plt.xlabel('num PCA Components')
plt.ylabel('Validation Accuracy')
plt.title('Accuracy as function of num PCs')

Text(0.5,1,'Accuracy as function of num PCs')

Overall, it's evident that all the classifiers achieve similar performance approximately.

But it's interesting to note different behaviors of the classifiers as a function of number of Principal Components used.

3.3.3 t-SNE Visualization

print('Images Shape')
print('Before Scaling:', images.shape)

# Let's scale images using StandardScaler

images_scaled = StandardScaler().fit_transform(images)
print('After Scaling:', images_scaled.shape)
print('\nLabels Shape:', label_ids.shape)

Images Shape
Before Scaling: (4750, 2025)
After Scaling: (4750, 2025)

Labels Shape: (4750,)

Now let's do PCA using the scaled images and reduce the dimesions from 2025 to 180.

# PCA Analysis

pca = PCA(n_components=180)
pca_result = pca.fit_transform(images_scaled)

pca_result.shape

(4750, 180)

# TSNE Analysis

tsne = TSNE(n_components=2, perplexity=40.0)
tsne_result = tsne.fit_transform(pca_result)

# Visualize seedlings clusters in a scatter plot

visualize_scatter(tsne_result, label_ids)

# Let's scale images using StandardScaler

tsne_result_scaled = StandardScaler().fit_transform(tsne_result)
tsne_result_scaled.shape

(4750, 2)

# Visualize seedlings clusters in a scatter plot

visualize_scatter(tsne_result_scaled, label_ids)

# Visualize seedlings clusters in a scatter plot along with images

visualize_scatter_with_images(
    tsne_result_scaled,
    images=[np.reshape(i, (45, 45)) for i in images],
    image_zoom=0.7)

3D t-SNE

# TSNE Analysis

tsne = TSNE(n_components=3)
tsne_result = tsne.fit_transform(pca_result)
tsne_result_scaled = StandardScaler().fit_transform(tsne_result)

# Visualize seedlings clusters in a 3D scatter plot

visualize_scatter_3D(tsne_result_scaled, label_ids)

# Visualize seedlings clusters in a 3D scatter plot with animation GIF

import io
import base64
from IPython.display import HTML

video = io.open(os.path.join('./visualization', 'seedlings_3D.gif'),
                'r+b').read()
HTML(data='''<img src="data:image/gif;base64,{0}" type="gif" />'''.format(
    base64.b64encode(video).decode('ascii')))

3.4 Normalization

Now we can normalize an image RGB color space values raning from [0...255] to [0...1]. It will loose some discrimantory features but CNN being compute intensive will train faster.

image_norm = image/255

It seems that there isn't a big improvements from normalization.

4. Modelling

Now we are ready to train a model and predict. There are loads of predictive modelling algorithms to choose from. We must understand the type of problem and solution requirements to narrow down to a select few baseline models which we can evaluate iteratively.

Our problem falls in the supervised classification category. We want to identify the relationship between a seedling class and an input image.

With above two criteria - Supervised Learning plus Image Classification, we can narrow down our choice of models to a few which are as follow:

1. CNN
2. Transfer Learning using Xception, VGG, ImageNet
3. XGB using only countour features

4.1 CNN

The convolutional Neural Networks has the following strutctures.

• Input eg. (51x51x3)
• First 64 filters
• Second 64 filters
• Third 128 filters
• Fourth 128 filters
• Fifth 256 filters
• Sixth 256 filters
• Seventh 256 filters
• Eigth Dense layer 128 neurons
• Output 12 Seedling classes

Each pair of convolution layers have a max pooling layer and uses batch normalization to reduce overfitting. It also takes advantage of Leaky ReLUs. In dense layers, we will use dropout to reduce overfitting.

In the end, the fully-connected layers will be used for classification which the outputs probability distribution for each of 12 Seedling classes.

Batch normalization is a technique to provide any layer in a neural network with inputs that are zero mean/unit variance. It is used to normalize the input layer by adjusting and scaling the activations.

Leaky ReLUs allow a small, non-zero gradient when the unit is not active.

Parametric ReLUs take this idea further by making the coefficient of leakage into a parameter that is learned along with the other neural network parameters.

4.1.1 Fixed Variables Initialization

BATCH_SIZE = 16
EPOCHS = 16
RANDOM_STATE = 21

# Same as label_to_id_dict

CLASS = {
    'Black-grass': 0,
    'Charlock': 1,
    'Cleavers': 2,
    'Common Chickweed': 3,
    'Common wheat': 4,
    'Fat Hen': 5,
    'Loose Silky-bent': 6,
    'Maize': 7,
    'Scentless Mayweed': 8,
    'Shepherds Purse': 9,
    'Small-flowered Cranesbill': 10,
    'Sugar beet': 11
}

# Same as id_to_label_dict

INV_CLASS = {
    0: 'Black-grass',
    1: 'Charlock',
    2: 'Cleavers',
    3: 'Common Chickweed',
    4: 'Common wheat',
    5: 'Fat Hen',
    6: 'Loose Silky-bent',
    7: 'Maize',
    8: 'Scentless Mayweed',
    9: 'Shepherds Purse',
    10: 'Small-flowered Cranesbill',
    11: 'Sugar beet'
}

4.1.2 Define Model Network

# Define Dense layers


def dense_layer(inputs, neurons, activation, drop_rate=0.):
    dropout = Dropout(drop_rate)(inputs)
    fc = Dense(neurons)(dropout)
    fc_norm = BatchNormalization(axis=-1)(fc)
    outputs = Activation(activation=activation)(fc_norm)
    return outputs


# Define Convolution layers


def conv_layer(feature_batch,
               feature_map,
               kernel_size=(3, 3),
               strides=(1, 1),
               padding=False):
    if padding:
        processed_feature_batch = ZeroPadding2D((1, 1))(feature_batch)
    else:
        processed_feature_batch = feature_batch

    conv = Conv2D(
        filters=feature_map, kernel_size=kernel_size,
        strides=strides)(processed_feature_batch)
    conv_norm = BatchNormalization(axis=3)(conv)
    outputs = LeakyReLU(1 / 10)(conv_norm)
    return outputs


# Define Model Neural Network


def model_neural_network():

    inputs = Input(shape=(51, 51, 3))

    # First, Second layer
    conv1 = conv_layer(inputs, 64, padding=False)
    conv2 = conv_layer(conv1, 64, padding=False)
    pool1 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(conv2)

    # Third, Fourth layer
    conv3 = conv_layer(pool1, 128, padding=False)
    conv4 = conv_layer(conv3, 128, padding=False)
    pool2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(conv4)

    # Sixth, Seventh, Ninth layer
    conv7 = conv_layer(pool2, 256, padding=False)
    conv8 = conv_layer(conv7, 256, padding=False)
    conv9 = conv_layer(conv8, 256, padding=False)
    pool3 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(conv9)

    # Two Dense layers
    flattened = Flatten()(pool3)
    fc_output = dense_layer(flattened, 128, activation='tanh')
    outputs = dense_layer(fc_output, 12, activation='softmax')

    model = Model(inputs=inputs, outputs=outputs)

    # The first 50 epochs with Adam opt & then 30 epochs with SGD opt.

    #mypotim = Adam(lr=2 * 1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
    mypotim = SGD(lr=1 * 1e-1, momentum=0.9, nesterov=True)
    model.compile(
        loss='categorical_crossentropy',
        optimizer=mypotim,
        metrics=['accuracy'])
    model.summary()

    return model


# Model Callbacks


def get_callbacks(checkpoint_path, patience=5):

    if not os.path.exists('./logs'):
        os.makedirs('./logs')

    lr_reduced = ReduceLROnPlateau(
        monitor='val_acc',
        factor=0.1,
        epsilon=1e-5,
        patience=patience,
        verbose=1)
    checkpoint = ModelCheckpoint(
        checkpoint_path, monitor='val_acc', save_best_only=True, verbose=1)
    #early_stop = EarlyStopping(monitor='val_acc', patience=patience, mode='max')
    tensorboard = TensorBoard(
        log_dir='./logs',
        histogram_freq=2000,
        write_graph=True,
        write_images=True)

    return [lr_reduced, checkpoint, tensorboard]

train_dict = joblib.load(os.path.join('./save/pickles', 'train_dict.pickle'))
train_dict['class'][0:5]

['Sugar beet', 'Sugar beet', 'Sugar beet', 'Sugar beet', 'Sugar beet']

4.1.3 Training CNN Model

# Define Train method


def train(img, target, best_model):

    # Setting checkpoint & early stopping
    # Load the last best checkpoint files
    callbacks_list = get_callbacks(
        checkpoint_path=os.path.join(
            './checkpoint', 'model_weight_SGD.{epoch:02d}-{val_acc:.4f}.hdf5'),
        patience=5)

    # Initialize the model neural networks architecture
    model = model_neural_network()

    # Load network weights
    if os.path.exists(os.path.join('./checkpoint', best_model)):
        print('Loading weights from previous run...', best_model)
        model.load_weights(filepath=os.path.join('./checkpoint', best_model))
    else:
        print('Weights not loaded.', best_model)

    # Split the data in train & validation set
    x_train, x_valid, y_train, y_valid = train_test_split(
        img,
        target,
        shuffle=True,
        train_size=0.8,
        stratify=target,
        random_state=RANDOM_STATE)
    # To avoide overfitting, we need to create an image generator which will randomly rotate, zoom, shift and flip image
    # during the fitting of the model.
    # Define an ImageDataGenerator
    image_gen = ImageDataGenerator(
        rotation_range=360.,
        width_shift_range=0.3,
        height_shift_range=0.3,
        zoom_range=0.3,
        horizontal_flip=True,
        vertical_flip=True)

    # Fitting the model with the image generator & others
    model.fit_generator(
        image_gen.flow(
            x_train, y_train, batch_size=BATCH_SIZE),
        steps_per_epoch=10 * len(x_train) / BATCH_SIZE,
        epochs=EPOCHS,
        verbose=1,
        shuffle=True,
        validation_data=(x_valid, y_valid),
        callbacks=callbacks_list)


# Training the model


def train_main():

    try:
        train_dict = joblib.load(
            os.path.join('./save/pickles', 'train_dict.pickle'))
        test_dict = joblib.load(
            os.path.join('./save/pickles', 'test_dict.pickle'))
        print('Loading train & test dict...')
    except Exception as e:
        print('Stacktrace', e)
        print('Train & Test Dict does not exist.')

    if (not train_dict) and (not test_dict):
        train_dict, test_dict = reader()
        # Dumping dicts
        joblib.dump(train_dict,
                    os.path.join('./save/pickles', 'train_dict.pickle'))
        joblib.dump(test_dict,
                    os.path.join('./save/pickles', 'test_dict.pickle'))

    X_train = np.array(train_dict['image'])
    y_train = to_categorical(np.array([CLASS[l] for l in train_dict['class']]))

    best_model = 'model_weight_SGD.05-0.9526.hdf5'
    train(X_train, y_train, best_model)


if __name__ == '__main__':
    train_main()

Loading train & test dict...
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 51, 51, 3)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 49, 49, 64)        1792      
_________________________________________________________________
batch_normalization_1 (Batch (None, 49, 49, 64)        256       
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 49, 49, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 47, 47, 64)        36928     
_________________________________________________________________
batch_normalization_2 (Batch (None, 47, 47, 64)        256       
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 47, 47, 64)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 23, 23, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 21, 21, 128)       73856     
_________________________________________________________________
batch_normalization_3 (Batch (None, 21, 21, 128)       512       
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 21, 21, 128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 19, 19, 128)       147584    
_________________________________________________________________
batch_normalization_4 (Batch (None, 19, 19, 128)       512       
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU)    (None, 19, 19, 128)       0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 9, 9, 128)         0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 7, 7, 256)         295168    
_________________________________________________________________
batch_normalization_5 (Batch (None, 7, 7, 256)         1024      
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 5, 5, 256)         590080    
_________________________________________________________________
batch_normalization_6 (Batch (None, 5, 5, 256)         1024      
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 5, 5, 256)         0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 3, 3, 256)         590080    
_________________________________________________________________
batch_normalization_7 (Batch (None, 3, 3, 256)         1024      
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU)    (None, 3, 3, 256)         0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 1, 1, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 256)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               32896     
_________________________________________________________________
batch_normalization_8 (Batch (None, 128)               512       
_________________________________________________________________
activation_1 (Activation)    (None, 128)               0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 12)                1548      
_________________________________________________________________
batch_normalization_9 (Batch (None, 12)                48        
_________________________________________________________________
activation_2 (Activation)    (None, 12)                0         
=================================================================
Total params: 1,775,100
Trainable params: 1,772,516
Non-trainable params: 2,584
_________________________________________________________________
Loading weights from previous run... model_weight_SGD.05-0.9526.hdf5
Epoch 1/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1436 - acc: 0.9488Epoch 00001: val_acc improved from -inf to 0.94842, saving model to ./checkpoint/model_weight_SGD.01-0.9484.hdf5
2375/2375 [==============================] - 1342s 565ms/step - loss: 0.1436 - acc: 0.9488 - val_loss: 0.1180 - val_acc: 0.9484
Epoch 2/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1434 - acc: 0.9504Epoch 00002: val_acc did not improve
2375/2375 [==============================] - 1255s 528ms/step - loss: 0.1434 - acc: 0.9504 - val_loss: 0.1454 - val_acc: 0.9432
Epoch 3/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1360 - acc: 0.9506Epoch 00003: val_acc improved from 0.94842 to 0.96316, saving model to ./checkpoint/model_weight_SGD.03-0.9632.hdf5
2375/2375 [==============================] - 1296s 546ms/step - loss: 0.1360 - acc: 0.9506 - val_loss: 0.0954 - val_acc: 0.9632
Epoch 4/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1357 - acc: 0.9525Epoch 00004: val_acc did not improve
2375/2375 [==============================] - 1299s 547ms/step - loss: 0.1357 - acc: 0.9525 - val_loss: 0.2628 - val_acc: 0.9000
Epoch 5/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1298 - acc: 0.9533Epoch 00007: val_acc did not improve
2375/2375 [==============================] - 1327s 559ms/step - loss: 0.1298 - acc: 0.9533 - val_loss: 0.2141 - val_acc: 0.9200
Epoch 8/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1219 - acc: 0.9559Epoch 00010: val_acc did not improve
2375/2375 [==============================] - 1330s 560ms/step - loss: 0.1219 - acc: 0.9559 - val_loss: 0.2393 - val_acc: 0.9105
Epoch 11/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.1209 - acc: 0.9567
Epoch 00011: reducing learning rate to 0.010000000149011612.
Epoch 00011: val_acc did not improve
2375/2375 [==============================] - 1331s 560ms/step - loss: 0.1209 - acc: 0.9567 - val_loss: 0.0733 - val_acc: 0.9737
Epoch 12/16
2374/2375 [============================>.] - ETA: 0s - loss: 0.0948 - acc: 0.9660Epoch 00012: val_acc improved from 0.97368 to 0.98316, saving model to ./checkpoint/model_weight_SGD.12-0.9832.hdf5
2375/2375 [==============================] - 1326s 558ms/step - loss: 0.0948 - acc: 0.9660 - val_loss: 0.0594 - val_acc: 0.9832
Epoch 13/16
1268/2375 [===============>..............] - ETA: 10:12 - loss: 0.0874 - acc: 0.9678

4.1.4 Evaluate Model

On Validation Set

# Define Test method


def test_valid(X_train, x_valid, y_train, y_valid, best_model):

    # Initialize the model neural networks architecture & load weights
    model = model_neural_network()
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))

    # Evaluate on train and valid set
    print('\nCalculating Train Set accuracy ...')
    train_acc = model.evaluate(
        X_train, to_categorical(np.array([CLASS[l] for l in y_train])))
    print('Train Set accuracy: {}%'.format(
        [np.round(acc * 100, 2) for acc in train_acc]))

    print('\nCalculating Valid Set accuracy ...')
    valid_acc = model.evaluate(
        x_valid, to_categorical(np.array([CLASS[l] for l in y_valid])))
    print('Valid Set accuracy: {}%'.format(
        [np.round(acc * 100, 2) for acc in valid_acc]))

    print('\nCalculating Confusion Matrix & Classification Report ...')
    # Predictions on the valid set
    prob = model.predict(x_valid, verbose=1)
    predictions = prob.argmax(axis=1)

    # True class label
    true = np.array([CLASS[l] for l in y_valid])

    # Confusion matrix on the valid set.
    con_matrix = confusion_matrix(true, predictions)

    print('\n\nConfusion matrix of the Seedlings Valid set\n\n')
    display(
        pd.DataFrame(
            con_matrix, columns=list(CLASS.keys()), index=list(CLASS.keys())))

    # Plot the confusion matrix
    plot_confusion_matrix(
        con_matrix,
        target_names=list(CLASS.keys()),
        plt_name='Seedlings_Con_Matrix',
        rootdir='./',
        save_dir='save/con_matrix',
        title='Confusion matrix',
        cmap=plt.cm.Blues,
        normalize=False)

    print('\n\nClassification Report')
    print(
        classification_report(
            true, predictions, target_names=list(CLASS.keys())))


# Testing the model on validation set


def valid_main():

    try:
        train_dict = joblib.load(
            os.path.join('./save/pickles', 'train_dict.pickle'))
        test_dict = joblib.load(
            os.path.join('./save/pickles', 'test_dict.pickle'))
        print('Loading train & test dict...')
    except Exception as e:
        print('Stacktrace', e)
        print('Train & Test Dict does not exist.')

    if (not train_dict) and (not test_dict):
        train_dict, test_dict = reader()
        # Dumping dicts
        joblib.dump(train_dict,
                    os.path.join('./save/pickles', 'train_dict.pickle'))
        joblib.dump(test_dict,
                    os.path.join('./save/pickles', 'test_dict.pickle'))

    img = np.array(train_dict['image'])
    label = train_dict['label']
    target = train_dict['class']

    # Split the data in train & validation set
    X_train, x_valid, y_train, y_valid = train_test_split(
        img,
        target,
        shuffle=True,
        train_size=0.7,
        stratify=target,
        random_state=RANDOM_STATE)

    # best_model = input('Enter the name of best model saved in ./logs dir: ')
    best_model = 'model_weight_SGD.12-0.9832.hdf5'
    test_valid(X_train, x_valid, y_train, y_valid, best_model)


if __name__ == '__main__':
    valid_main()

Loading train & test dict...
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_12 (InputLayer)        (None, 51, 51, 3)         0         
_________________________________________________________________
conv2d_78 (Conv2D)           (None, 49, 49, 64)        1792      
_________________________________________________________________
batch_normalization_100 (Bat (None, 49, 49, 64)        256       
_________________________________________________________________
leaky_re_lu_78 (LeakyReLU)   (None, 49, 49, 64)        0         
_________________________________________________________________
conv2d_79 (Conv2D)           (None, 47, 47, 64)        36928     
_________________________________________________________________
batch_normalization_101 (Bat (None, 47, 47, 64)        256       
_________________________________________________________________
leaky_re_lu_79 (LeakyReLU)   (None, 47, 47, 64)        0         
_________________________________________________________________
max_pooling2d_34 (MaxPooling (None, 23, 23, 64)        0         
_________________________________________________________________
conv2d_80 (Conv2D)           (None, 21, 21, 128)       73856     
_________________________________________________________________
batch_normalization_102 (Bat (None, 21, 21, 128)       512       
_________________________________________________________________
leaky_re_lu_80 (LeakyReLU)   (None, 21, 21, 128)       0         
_________________________________________________________________
conv2d_81 (Conv2D)           (None, 19, 19, 128)       147584    
_________________________________________________________________
batch_normalization_103 (Bat (None, 19, 19, 128)       512       
_________________________________________________________________
leaky_re_lu_81 (LeakyReLU)   (None, 19, 19, 128)       0         
_________________________________________________________________
max_pooling2d_35 (MaxPooling (None, 9, 9, 128)         0         
_________________________________________________________________
conv2d_82 (Conv2D)           (None, 7, 7, 256)         295168    
_________________________________________________________________
batch_normalization_104 (Bat (None, 7, 7, 256)         1024      
_________________________________________________________________
leaky_re_lu_82 (LeakyReLU)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_83 (Conv2D)           (None, 5, 5, 256)         590080    
_________________________________________________________________
batch_normalization_105 (Bat (None, 5, 5, 256)         1024      
_________________________________________________________________
leaky_re_lu_83 (LeakyReLU)   (None, 5, 5, 256)         0         
_________________________________________________________________
conv2d_84 (Conv2D)           (None, 3, 3, 256)         590080    
_________________________________________________________________
batch_normalization_106 (Bat (None, 3, 3, 256)         1024      
_________________________________________________________________
leaky_re_lu_84 (LeakyReLU)   (None, 3, 3, 256)         0         
_________________________________________________________________
max_pooling2d_36 (MaxPooling (None, 1, 1, 256)         0         
_________________________________________________________________
flatten_12 (Flatten)         (None, 256)               0         
_________________________________________________________________
dropout_23 (Dropout)         (None, 256)               0         
_________________________________________________________________
dense_23 (Dense)             (None, 128)               32896     
_________________________________________________________________
batch_normalization_107 (Bat (None, 128)               512       
_________________________________________________________________
activation_23 (Activation)   (None, 128)               0         
_________________________________________________________________
dropout_24 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_24 (Dense)             (None, 12)                1548      
_________________________________________________________________
batch_normalization_108 (Bat (None, 12)                48        
_________________________________________________________________
activation_24 (Activation)   (None, 12)                0         
=================================================================
Total params: 1,775,100
Trainable params: 1,772,516
Non-trainable params: 2,584
_________________________________________________________________

Calculating Train Set accuracy ...
3325/3325 [==============================] - 37s 11ms/step
Train Set accuracy: [3.57, 98.89]%

Calculating Valid Set accuracy ...
1425/1425 [==============================] - 16s 11ms/step
Valid Set accuracy: [4.12, 98.74]%

Calculating Confusion Matrix & Classification Report ...
1425/1425 [==============================] - 16s 11ms/step


Confusion matrix of the Seedlings Valid set

	Sugar beet	Cleavers	Scentless Mayweed	Fat Hen	Common Chickweed	Loose Silky-bent	Black-grass	Charlock	Maize	Small-flowered Cranesbill	Common wheat	Shepherds Purse
Sugar beet	70	0	0	0	0	0	9	0	0	0	0	0
Cleavers	0	117	0	0	0	0	0	0	0	0	0	0
Scentless Mayweed	0	0	85	0	0	0	1	0	0	0	0	0
Fat Hen	0	0	0	183	0	0	0	0	0	0	0	0
Common Chickweed	0	0	0	0	66	0	0	0	0	0	0	0
Loose Silky-bent	0	0	0	0	0	143	0	0	0	0	0	0
Black-grass	7	0	0	0	0	0	189	0	0	0	0	0
Charlock	0	0	0	0	0	0	0	66	0	0	0	0
Maize	0	0	0	0	0	0	0	0	155	0	0	0
Small-flowered Cranesbill	0	0	0	0	0	0	0	0	0	69	0	0
Common wheat	0	0	0	0	0	0	0	0	0	0	149	0
Shepherds Purse	0	0	0	1	0	0	0	0	0	0	0	115

Saving Confusion Matrices in the ./save/con_matrix directory

Classification Report
                           precision    recall  f1-score   support

               Sugar beet       0.91      0.89      0.90        79
                 Cleavers       1.00      1.00      1.00       117
        Scentless Mayweed       1.00      0.99      0.99        86
                  Fat Hen       0.99      1.00      1.00       183
         Common Chickweed       1.00      1.00      1.00        66
         Loose Silky-bent       1.00      1.00      1.00       143
              Black-grass       0.95      0.96      0.96       196
                 Charlock       1.00      1.00      1.00        66
                    Maize       1.00      1.00      1.00       155
Small-flowered Cranesbill       1.00      1.00      1.00        69
             Common wheat       1.00      1.00      1.00       149
          Shepherds Purse       1.00      0.99      1.00       116

              avg / total       0.99      0.99      0.99      1425

Submission on Test Set

# Define Test method


def test(img, label, best_model):

    # Initialize the model neural networks architecture & load weights
    model = model_neural_network()
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))

    # Predictions
    prob = model.predict(img, verbose=1)
    pred = prob.argmax(axis=-1)

    submission_df = pd.DataFrame({
        "file": label,
        "species": [INV_CLASS[p] for p in pred]
    })
    submission_df.to_csv(
        os.path.join('./save', 'submission.csv'), index=False, header=True)


# Testing the model


def test_main():

    try:
        test_dict = joblib.load(
            os.path.join('./save/pickles', 'test_dict.pickle'))
        print('Loading dict...')
    except Exception as e:
        print('Stacktrace', e)
        print('Test Dict does not exist.')

    if not test_dict:
        _, test_dict = reader()

    X_test = np.array(test_dict['image'])
    label = test_dict['label']

    best_model = input('Enter the name of best model saved in ./logs dir: ')
    test(X_test, label, best_model)


if __name__ == '__main__':
    test_main()

Loading dict...

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 51, 51, 3)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 49, 49, 64)        1792      
_________________________________________________________________
batch_normalization_1 (Batch (None, 49, 49, 64)        256       
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 49, 49, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 47, 47, 64)        36928     
_________________________________________________________________
batch_normalization_2 (Batch (None, 47, 47, 64)        256       
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 47, 47, 64)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 23, 23, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 21, 21, 128)       73856     
_________________________________________________________________
batch_normalization_3 (Batch (None, 21, 21, 128)       512       
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 21, 21, 128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 19, 19, 128)       147584    
_________________________________________________________________
batch_normalization_4 (Batch (None, 19, 19, 128)       512       
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU)    (None, 19, 19, 128)       0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 9, 9, 128)         0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 7, 7, 256)         295168    
_________________________________________________________________
batch_normalization_5 (Batch (None, 7, 7, 256)         1024      
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 5, 5, 256)         590080    
_________________________________________________________________
batch_normalization_6 (Batch (None, 5, 5, 256)         1024      
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 5, 5, 256)         0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 3, 3, 256)         590080    
_________________________________________________________________
batch_normalization_7 (Batch (None, 3, 3, 256)         1024      
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU)    (None, 3, 3, 256)         0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 1, 1, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 256)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               32896     
_________________________________________________________________
batch_normalization_8 (Batch (None, 128)               512       
_________________________________________________________________
activation_1 (Activation)    (None, 128)               0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 12)                1548      
_________________________________________________________________
batch_normalization_9 (Batch (None, 12)                48        
_________________________________________________________________
activation_2 (Activation)    (None, 12)                0         
=================================================================
Total params: 1,775,100
Trainable params: 1,772,516
Non-trainable params: 2,584
_________________________________________________________________
794/794 [==============================] - 10s 12ms/step

4.2 Transfer Learning using Xception, VGG, ImageNet

Use Keras Pretrained Models

%ls ../../models/keras-pretrained-models

imagenet_class_index.json
inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5
inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5
inception_v3_weights_tf_dim_ordering_tf_kernels.h5
inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
Kuszma.JPG
resnet50_weights_tf_dim_ordering_tf_kernels.h5
resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
xception_weights_tf_dim_ordering_tf_kernels.h5
xception_weights_tf_dim_ordering_tf_kernels_notop.h5

Helper Functions to load Seedlings data

# Load test seedlings data & return images, labels, image_per_class


def load_seedlings_transfer(path=os.path.join(root_dir, 'train'),
                            img_size=(299, 299)):
    images = []
    labels = []
    images_per_class = defaultdict(list)

    for class_folder_name in os.listdir(path):
        class_folder_path = os.path.join(path, class_folder_name)

        for image_path in glob(os.path.join(class_folder_path, "*.png")):
            # Read an image
            image = cv2.imread(image_path, cv2.IMREAD_COLOR)

            # Stats for each seedlings class
            images_per_class[class_folder_name].append(image)

            # Resize image to img_size pixels
            image = cv2.resize(image, img_size, interpolation=cv2.INTER_AREA)

            images.append(image)
            labels.append(class_folder_name)

    images = np.array(images)
    labels = np.array(labels)

    return images, labels, images_per_class


# Load test seedlings data & return images, ids


def load_seedlings_transfer_test(path=os.path.join(root_dir, 'test'),
                                 img_size=(299, 299)):
    images = []
    ids = []

    for image_path in glob(os.path.join(path, "*.png")):
        # Read an image
        image = cv2.imread(image_path, cv2.IMREAD_COLOR)

        # Resize image to img_size pixels
        image = cv2.resize(image, img_size, interpolation=cv2.INTER_AREA)

        images.append(image)
        ids.append(os.path.basename(image_path))

    images = np.array(images)
    ids = np.array(ids)

    return images, ids

ls data/all/

sample_submission.csv  test/  test.zip  train/  train.zip

4.2.1 Xception Model

4.2.1.1 Prepare & Load Data

The Xception model input dimesions are 299 * 299 * 3. Hence we need to prepare images satisfying the dimensional requirements.

# Constants

input_dim = 299
POOLING = 'avg'
RANDOM_STATE = 21

images_xception = np.array([])

try:
    print('Loading Images & Labels dict ...')
    images_xception = joblib.load(os.path.join(
        './save/pickles', 'images-xception.pickle'))
    labels = joblib.load(os.path.join(
        './save/pickles', 'labels-xception.pickle'))
    print('Loading done.')
except Exception as e:
    print('Stacktrace', e)
    print('Images & Labels dict does not exist.')

if not (images_xception.size):
    img_size = (299, 299)
    print('Running load_seedlings method with img_size {} ...'.format(img_size))
    images_xception, labels, images_per_class = load_seedlings_transfer(
        img_size=img_size)

    print('Dumping Images & Labels dict ...')
    # Dumping dicts
    joblib.dump(images_xception, os.path.join(
        './save/pickles', 'images-xception.pickle'))
    joblib.dump(labels, os.path.join(
        './save/pickles', 'labels-xception.pickle'))
    print('Images & Labels dict dumping done.')

Loading Images, Labels & images_per_class dict ...
Loading done.

# Type cast int to float for Xception model compatibility

images_xception = images_xception.astype(dtype='float32', copy=False)

print('Images Shape:', images_xception.shape)
print('Image Data Type:', images_xception[0].dtype)
print('Labels Shape:', labels.shape)
print('Label Data Type:', labels[0].dtype)

Images Shape: (4750, 299, 299, 3)
Image Data Type: float32
Labels Shape: (4750,)
Label Data Type: <U10

4.2.1.2 Extract Xception Bottleneck Features

%time

# Numpy array to store extracted features from Xception model
images_xception_features = np.zeros(
    (len(images_xception), input_dim, input_dim, 3), dtype='float32')

# Extract features with Xception model
for i, image in tqdm(enumerate(images_xception)):
    x = xception.preprocess_input(np.expand_dims(image, axis=0))
    images_xception_features[i] = x

print('Train Images shape: {} size: {:,}'.format(
    images_xception_features.shape, images_xception_features.size))

print('Dumping images_xception_features ...')
joblib.dump(images_xception_features, os.path.join(
    './save/pickles', 'images_xception_features.pickle'))
print('images_xception_features dumping done.')

126it [00:00, 1254.04it/s]

CPU times: user 1 µs, sys: 1 µs, total: 2 µs
Wall time: 3.81 µs

4750it [00:02, 1652.94it/s]

Train Images shape: (4750, 299, 299, 3) size: 1,273,964,250

del images_xception_features, labels

try:
    print('Loading images_xception_features & Labels ...')
    images_xception_features = joblib.load(os.path.join(
        './save/pickles', 'images_xception_features.pickle'))
    labels = joblib.load(os.path.join(
        './save/pickles', 'labels-xception.pickle'))
    print('Loading done.')
except Exception as e:
    print('Stacktrace', e)
    print('images_xception_features & Labels does not exist.')


# Split the data in train & validation set
x_train, x_valid, y_train, y_valid = train_test_split(
    images_xception_features,
    labels,
    shuffle=True,
    train_size=0.8,
    stratify=labels,
    random_state=RANDOM_STATE
)

del images_xception_features, labels

# Xception V1 model, with weights pre-trained on ImageNet. On ImageNet, this model gets to
# a top-1 validation accuracy of 0.790 and a top-5 validation accuracy of 0.945. Note that this
# model only supports the data format 'channels_last' (height, width, channels).
# The default input size for this model is 299x299.

xception_bottleneck = xception.Xception(
    weights='imagenet', include_top=False, pooling=POOLING)

# Calculating Xception train & valid set bottleneck features
x_train_botf = xception_bottleneck.predict(x_train, batch_size=32, verbose=1)
x_valid_botf = xception_bottleneck.predict(x_valid, batch_size=32, verbose=1)

print('Xception train bottleneck features shape: {} size: {:,}'.format(
    x_train_botf.shape, x_train_botf.size))
print('Xception valid bottleneck features shape: {} size: {:,}'.format(
    x_valid_botf.shape, x_valid_botf.size))

# Dumping Xception train & valid set bottleneck features
joblib.dump(x_train_botf, os.path.join(
    './save/pickles', 'x_train_botf.pickle'))
joblib.dump(x_valid_botf, os.path.join(
    './save/pickles', 'x_valid_botf.pickle'))

joblib.dump(y_train, os.path.join('./save/pickles', 'y_train_botf.pickle'))
joblib.dump(y_valid, os.path.join('./save/pickles', 'y_valid_botf.pickle'))
print('Dumping Xception train & valid set bottleneck features & labels done.')

Loading images_xception_features & Labels ...
Loading done.
3800/3800 [==============================] - 1794s 472ms/step
950/950 [==============================] - 443s 467ms/step
Xception train bottleneck features shape: (3800, 2048) size: 7,782,400
Xception valid bottleneck features shape: (950, 2048) size: 1,945,600
Dumping Xception train & valid set bottleneck features done.

4.2.1.3 Logistic Regression on Xception Bottleneck Features

# Logistic Regression classifier

logreg = LogisticRegression(
    multi_class='multinomial', solver='lbfgs', random_state=RANDOM_STATE)
logreg.fit(x_train_botf, y_train)

valid_probs = logreg.predict_proba(x_valid_botf)
valid_preds = logreg.predict(x_valid_botf)

print('Logistic Regression model Accuracy on valid set using Xception Bottleneck Features: {}%'.format(
    np.round(accuracy_score(y_valid, valid_preds)*100, 2)))

Logistic Regression model Accuracy on valid set using Xception Bottleneck Features: 87.26%

Confusion Matrix

con_matrix = confusion_matrix(y_valid, valid_preds)

# Plot confusion matrix using seaborn heatmap

fig, ax = plt.subplots(1, figsize=(15, 15))

ax = sns.heatmap(con_matrix, ax=ax, cmap=plt.cm.Oranges, annot=True)

ax.set_xticklabels(list(CLASS.keys()), rotation=45)
ax.set_yticklabels(list(CLASS.keys()), rotation=45)

plt.xlabel('Predicted class')
plt.ylabel('True class')
plt.title('Confusion Matrix')

fig.savefig(os.path.join('./save/con_matrix',
                         'Seedlings_logreg_Confusion_Matrix.png'), dpi=300)
plt.show()

4.2.1.4 Classifier Comparison

After fitting our train dataset on Xception Bottleneck Features, you can run a general Classifier Comparison module containing sklearn classifers with vanilla configurations.

% % time


def classifier_comparison(n_splits=3):
    # Classifier names & initializations
    names = ["Nearest Neighbors", "SVM", "Gaussian Process",
             "Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "GradientBoost"
             "Naive Bayes", "LDA", "QDA", "XGB Classifier"]

    classifiers = [
        KNeighborsClassifier(3),
        SVC(probability=True),
        GaussianProcessClassifier(),
        DecisionTreeClassifier(),
        RandomForestClassifier(),
        MLPClassifier(),
        AdaBoostClassifier(),
        GradientBoostingClassifier(),
        GaussianNB(),
        LinearDiscriminantAnalysis(),
        QuadraticDiscriminantAnalysis(),
        XGBClassifier()]

    # Split & shuffle data using Stratified K Fold
    kfold = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=21)

    # Calculate accuracy over all splits, classifiers
    count = 0
    acc_dict = defaultdict(list)
    for train_index, test_index in kfold.split(x_train_botf, y_train):
        xtrain, ytrain = x_train_botf[train_index], y_train[train_index]
        xtest, ytest = x_train_botf[test_index], y_train[test_index]
        print('Iteration: ', count)

        # iterate over classifiers
        for clf in classifiers:
            clf_name = clf.__class__.__name__
            clf.fit(xtrain, ytrain)
            predictions = clf.predict(xtest)
            accuracy = accuracy_score(ytest, predictions)

            # Append results in default dict of lists
            acc_dict[clf_name].append(accuracy)

        count += 1

    return acc_dict


# Call the classifier_comparison module & save the results
acc_dict = classifier_comparison(n_splits=3)

Iteration:  0

/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:442: UserWarning: The priors do not sum to 1. Renormalizing
  UserWarning)
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

Iteration:  1

/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

Iteration:  2

/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")
/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/discriminant_analysis.py:682: UserWarning: Variables are collinear
  warnings.warn("Variables are collinear")

CPU times: user 55min 50s, sys: 6min 1s, total: 1h 1min 52s
Wall time: 50min 47s

/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.
  if diff:

# Dataframe containing classifier name & accuracy score
acc_df = pd.DataFrame.from_dict(acc_dict, orient='index')
acc_df = acc_df.rename(
    columns={0: '1st Iteration', 1: '2nd Iteration', 2: '3rd Iteration'})

# Calculate average accuracy
acc_df['avg_acc'] = acc_df.mean(axis=1)
acc_df = (acc_df*100).round(2)

sorted_acc_df = acc_df.sort_values(by='avg_acc', ascending=False)
display(sorted_acc_df)

	1st Iteration	2nd Iteration	3rd Iteration	avg_acc
MLPClassifier	86.14	87.29	85.27	86.24
XGBClassifier	77.17	79.48	78.78	78.48
GradientBoostingClassifier	76.30	77.74	77.04	77.03
GaussianProcessClassifier	74.57	77.43	74.58	75.53
KNeighborsClassifier	73.31	75.22	73.40	73.97
LinearDiscriminantAnalysis	65.20	68.98	65.32	66.50
RandomForestClassifier	60.79	61.33	61.92	61.34
SVC	59.53	61.09	60.49	60.37
GaussianNB	51.50	54.70	56.53	54.24
DecisionTreeClassifier	48.43	48.38	48.54	48.45
AdaBoostClassifier	33.86	35.91	43.63	37.80
QuadraticDiscriminantAnalysis	13.23	13.50	12.51	13.08

# Plot a barplot containing classifier name & accuracy score

sns.barplot(x='avg_acc', y=sorted_acc_df.index, data=sorted_acc_df)
plt.xlabel('Average Accuracy')
plt.title('Classifier Comparison based on Accuracy')

Text(0.5,1,'Classifier Comparison based on Accuracy')

It seems like simple neural network MLP performs much better than others. Hence we can train a full blown Fully Connected networks in keras.

4.2.1.5 Training a Keras Dense Model

# Dumping Xception train & valid set bottleneck features

x_train_botf = joblib.load(os.path.join(
    './save/pickles', 'x_train_botf.pickle'))
x_valid_botf = joblib.load(os.path.join(
    './save/pickles', 'x_valid_botf.pickle'))

y_train_botf = joblib.load(os.path.join(
    './save/pickles', 'y_train_botf.pickle'))
y_valid_botf = joblib.load(os.path.join(
    './save/pickles', 'y_valid_botf.pickle'))
print('Loading Xception train & valid set bottleneck features & labels done.')

Loading Xception train & valid set bottleneck features & labels done.

% % time

BATCH_SIZE = 16
EPOCHS = 500
RANDOM_STATE = 21
# Fixing random seed
np.random.seed(RANDOM_STATE)

# Reduce LR On Plateau
lr_reduced = ReduceLROnPlateau(
    monitor='val_acc', factor=0.1, epsilon=1e-5, patience=5, verbose=1)

# Setting checkpoint & early stopping
checkpoint_path = './checkpoint/Xception_ADAM_best_weights.{epoch:02d}-{acc:.4f}.hdf5'
checkpoint = ModelCheckpoint(
    checkpoint_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')

# TensorBoard logs
tensorboard = TensorBoard(log_dir='./logs{}'.format(time()),
                          histogram_freq=2000, write_graph=True, write_images=True)

# Early Stopping
# early_stop = EarlyStopping(monitor='val_acc', patience=5, mode='max')

# All callbacks
callbacks_list = [lr_reduced, checkpoint, tensorboard]


# Model Neural Network Architecture
inputs = Input(shape=(2048,))

# Adding 1st Fully connected, output layer & Dropout
fc = Dense(input_shape=(2048,), units=2048, activation='relu')(inputs)
fc = Dropout(0.5)(fc)

# Adding 2nd Fully connected, output layer & Dropout
fc = Dense(input_shape=(1024,), units=1024, activation='relu')(fc)
fc = Dropout(0.4)(fc)

# Adding 3rd Fully connected, output layer & Dropout
fc = Dense(input_shape=(512,), units=512, activation='relu')(fc)
fc = Dropout(0.3)(fc)

# Adding 4th Fully connected, output layer & Dropout
fc = Dense(input_shape=(256,), units=256, activation='relu')(fc)
fc = Dropout(0.2)(fc)

# Adding 5th Fully connected, output layer & Dropout
fc = Dense(input_shape=(128,), units=128, activation='relu')(fc)
fc = Dropout(0.1)(fc)

# Adding output layer
outputs = Dense(units=12, activation='softmax')(fc)

model = Model(inputs=inputs, outputs=outputs)

best_model = 'Xception_ADAM_best_weights.41-0.9428.hdf5'
# Load network weights
if os.path.exists(os.path.join('./checkpoint', best_model)):
    print('Loading weights from previous run...', best_model)
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))
else:
    print('Weights not loaded.', best_model)


mypotim = Adam(lr=2 * 1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# mypotim = SGD(lr=1 * 1e-1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=mypotim,
              metrics=['accuracy'])
# Model Summary
model.summary()

# Convert categorical labels into one-hot vector
y_train_botf = to_categorical(np.array([CLASS[l] for l in y_train_botf]))

print('The Best Model will be saved in the following dir ', checkpoint_path)
print('Training of the model with Xception bottleneck features as inputs starts now ...')

# Fit the model
model.fit(x_train_botf, y_train_botf, validation_split=0.3, batch_size=BATCH_SIZE,
          epochs=EPOCHS, verbose=1, shuffle=True, callbacks=callbacks_list)

Loading weights from previous run... Xception_ADAM_best_weights.41-0.9428.hdf5
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 2048)              0         
_________________________________________________________________
dense_7 (Dense)              (None, 2048)              4196352   
_________________________________________________________________
dropout_6 (Dropout)          (None, 2048)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 1024)              2098176   
_________________________________________________________________
dropout_7 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 512)               524800    
_________________________________________________________________
dropout_8 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_10 (Dense)             (None, 256)               131328    
_________________________________________________________________
dropout_9 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_11 (Dense)             (None, 128)               32896     
_________________________________________________________________
dropout_10 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_12 (Dense)             (None, 12)                1548      
=================================================================
Total params: 6,985,100
Trainable params: 6,985,100
Non-trainable params: 0
_________________________________________________________________
The Best Model will be saved in the following dir  ./checkpoint/Xception_ADAM_best_weights.{epoch:02d}-{acc:.4f}.hdf5
Training of the model with Xception bottleneck features as inputs starts now ...
Train on 2660 samples, validate on 1140 samples
Epoch 1/500
2660/2660 [==============================] - 5s 2ms/step - loss: 0.7543 - acc: 0.8654 - val_loss: 0.6771 - val_acc: 0.8579

Epoch 00001: val_acc improved from -inf to 0.85789, saving model to ./checkpoint/Xception_ADAM_best_weights.01-0.8654.hdf5
Epoch 2/500
2660/2660 [==============================] - 3s 991us/step - loss: 0.7839 - acc: 0.8470 - val_loss: 0.7111 - val_acc: 0.8412

Epoch 00002: val_acc did not improve from 0.85789
Epoch 3/500
2660/2660 [==============================] - 3s 990us/step - loss: 1.0081 - acc: 0.7624 - val_loss: 0.6560 - val_acc: 0.8316

Epoch 00003: val_acc did not improve from 0.85789
Epoch 4/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.7773 - acc: 0.8034 - val_loss: 0.7376 - val_acc: 0.8246

Epoch 00004: val_acc did not improve from 0.85789
Epoch 5/500
2660/2660 [==============================] - 3s 1ms/step - loss: 1.0821 - acc: 0.7684 - val_loss: 0.7291 - val_acc: 0.8123

Epoch 00005: val_acc did not improve from 0.85789
Epoch 6/500
2660/2660 [==============================] - 3s 1ms/step - loss: 1.2182 - acc: 0.7361 - val_loss: 0.7179 - val_acc: 0.7904

Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.

Epoch 00006: val_acc did not improve from 0.85789
Epoch 7/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.8562 - acc: 0.7628 - val_loss: 0.6463 - val_acc: 0.8140

Epoch 00007: val_acc did not improve from 0.85789
Epoch 8/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.7347 - acc: 0.7962 - val_loss: 0.6238 - val_acc: 0.8140

Epoch 00008: val_acc did not improve from 0.85789
Epoch 9/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.7336 - acc: 0.8117 - val_loss: 0.5876 - val_acc: 0.8289

Epoch 00009: val_acc did not improve from 0.85789
Epoch 10/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.7027 - acc: 0.8135 - val_loss: 0.5784 - val_acc: 0.8377

Epoch 00010: val_acc did not improve from 0.85789
Epoch 11/500
2660/2660 [==============================] - 3s 945us/step - loss: 0.6331 - acc: 0.8244 - val_loss: 0.5804 - val_acc: 0.8465

Epoch 00011: ReduceLROnPlateau reducing learning rate to 2.0000000949949027e-05.

Epoch 00011: val_acc did not improve from 0.85789
Epoch 12/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5994 - acc: 0.8361 - val_loss: 0.5749 - val_acc: 0.8491

Epoch 00012: val_acc did not improve from 0.85789
Epoch 13/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5580 - acc: 0.8357 - val_loss: 0.5702 - val_acc: 0.8509

Epoch 00013: val_acc did not improve from 0.85789
Epoch 14/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5390 - acc: 0.8481 - val_loss: 0.5712 - val_acc: 0.8535

Epoch 00014: val_acc did not improve from 0.85789
Epoch 15/500
2660/2660 [==============================] - 3s 991us/step - loss: 0.5731 - acc: 0.8436 - val_loss: 0.5700 - val_acc: 0.8553

Epoch 00015: val_acc did not improve from 0.85789
Epoch 16/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5328 - acc: 0.8489 - val_loss: 0.5694 - val_acc: 0.8535

Epoch 00016: ReduceLROnPlateau reducing learning rate to 2.0000001313746906e-06.

Epoch 00016: val_acc did not improve from 0.85789
Epoch 17/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5728 - acc: 0.8466 - val_loss: 0.5688 - val_acc: 0.8535

Epoch 00017: val_acc did not improve from 0.85789
Epoch 18/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5688 - acc: 0.8455 - val_loss: 0.5686 - val_acc: 0.8535

Epoch 00018: val_acc did not improve from 0.85789
Epoch 19/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5954 - acc: 0.8447 - val_loss: 0.5685 - val_acc: 0.8535

Epoch 00019: val_acc did not improve from 0.85789
Epoch 20/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5790 - acc: 0.8432 - val_loss: 0.5671 - val_acc: 0.8535

Epoch 00020: val_acc did not improve from 0.85789
Epoch 21/500
2660/2660 [==============================] - 3s 984us/step - loss: 0.5746 - acc: 0.8534 - val_loss: 0.5668 - val_acc: 0.8535

Epoch 00021: ReduceLROnPlateau reducing learning rate to 2.000000222324161e-07.

Epoch 00021: val_acc did not improve from 0.85789
Epoch 22/500
2660/2660 [==============================] - 3s 989us/step - loss: 0.6100 - acc: 0.8365 - val_loss: 0.5668 - val_acc: 0.8535

Epoch 00022: val_acc did not improve from 0.85789
Epoch 23/500
2660/2660 [==============================] - 3s 987us/step - loss: 0.5515 - acc: 0.8526 - val_loss: 0.5668 - val_acc: 0.8535

Epoch 00023: val_acc did not improve from 0.85789
Epoch 24/500
2660/2660 [==============================] - 3s 997us/step - loss: 0.5334 - acc: 0.8553 - val_loss: 0.5667 - val_acc: 0.8535

Epoch 00024: val_acc did not improve from 0.85789
Epoch 25/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5331 - acc: 0.8406 - val_loss: 0.5667 - val_acc: 0.8535

Epoch 00025: val_acc did not improve from 0.85789
Epoch 26/500
2660/2660 [==============================] - 3s 997us/step - loss: 0.5203 - acc: 0.8489 - val_loss: 0.5666 - val_acc: 0.8544

Epoch 00026: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-08.

Epoch 00026: val_acc did not improve from 0.85789
Epoch 27/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5851 - acc: 0.8481 - val_loss: 0.5666 - val_acc: 0.8544

Epoch 00027: val_acc did not improve from 0.85789
Epoch 28/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5781 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8544

Epoch 00028: val_acc did not improve from 0.85789
Epoch 29/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5328 - acc: 0.8466 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00029: val_acc did not improve from 0.85789
Epoch 30/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5090 - acc: 0.8504 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00030: val_acc did not improve from 0.85789
Epoch 31/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5649 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00031: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-09.

Epoch 00031: val_acc did not improve from 0.85789
Epoch 32/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5755 - acc: 0.8376 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00032: val_acc did not improve from 0.85789
Epoch 33/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5814 - acc: 0.8440 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00033: val_acc did not improve from 0.85789
Epoch 34/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.4980 - acc: 0.8553 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00034: val_acc did not improve from 0.85789
Epoch 35/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5822 - acc: 0.8474 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00035: val_acc did not improve from 0.85789
Epoch 36/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5793 - acc: 0.8526 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00036: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-10.

Epoch 00036: val_acc did not improve from 0.85789
Epoch 37/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5129 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00037: val_acc did not improve from 0.85789
Epoch 38/500
2660/2660 [==============================] - 9s 3ms/step - loss: 0.5505 - acc: 0.8462 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00038: val_acc did not improve from 0.85789
Epoch 39/500
2660/2660 [==============================] - 10s 4ms/step - loss: 0.5440 - acc: 0.8530 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00039: val_acc did not improve from 0.85789
Epoch 40/500
2660/2660 [==============================] - 10s 4ms/step - loss: 0.5869 - acc: 0.8492 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00040: val_acc did not improve from 0.85789
Epoch 41/500
2660/2660 [==============================] - 10s 4ms/step - loss: 0.5308 - acc: 0.8466 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00041: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-11.

Epoch 00041: val_acc did not improve from 0.85789
Epoch 42/500
2660/2660 [==============================] - 10s 4ms/step - loss: 0.5506 - acc: 0.8414 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00042: val_acc did not improve from 0.85789
Epoch 43/500
2660/2660 [==============================] - 10s 4ms/step - loss: 0.5205 - acc: 0.8523 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00043: val_acc did not improve from 0.85789
Epoch 44/500
2660/2660 [==============================] - 12s 4ms/step - loss: 0.5301 - acc: 0.8508 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00044: val_acc did not improve from 0.85789
Epoch 45/500
2660/2660 [==============================] - 6s 2ms/step - loss: 0.5297 - acc: 0.8508 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00045: val_acc did not improve from 0.85789
Epoch 46/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5424 - acc: 0.8538 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00046: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-12.

Epoch 00046: val_acc did not improve from 0.85789
Epoch 47/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5723 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00047: val_acc did not improve from 0.85789
Epoch 48/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5579 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00048: val_acc did not improve from 0.85789
Epoch 49/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5272 - acc: 0.8511 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00049: val_acc did not improve from 0.85789
Epoch 50/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5562 - acc: 0.8477 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00050: val_acc did not improve from 0.85789
Epoch 51/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.5624 - acc: 0.8470 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00051: ReduceLROnPlateau reducing learning rate to 2.000000208848829e-13.

Epoch 00051: val_acc did not improve from 0.85789
Epoch 52/500
2660/2660 [==============================] - 3s 1ms/step - loss: 0.4917 - acc: 0.8455 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00052: val_acc did not improve from 0.85789
Epoch 53/500
2660/2660 [==============================] - 3s 997us/step - loss: 0.5349 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00053: val_acc did not improve from 0.85789
Epoch 54/500
2660/2660 [==============================] - 3s 959us/step - loss: 0.5540 - acc: 0.8530 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00054: val_acc did not improve from 0.85789
Epoch 55/500
2660/2660 [==============================] - 3s 955us/step - loss: 0.6032 - acc: 0.8376 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00055: val_acc did not improve from 0.85789
Epoch 56/500
2660/2660 [==============================] - 3s 955us/step - loss: 0.5838 - acc: 0.8402 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00056: ReduceLROnPlateau reducing learning rate to 2.0000002359538835e-14.

Epoch 00056: val_acc did not improve from 0.85789
Epoch 57/500
2660/2660 [==============================] - 3s 947us/step - loss: 0.5665 - acc: 0.8492 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00057: val_acc did not improve from 0.85789
Epoch 58/500
2660/2660 [==============================] - 3s 947us/step - loss: 0.5727 - acc: 0.8481 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00058: val_acc did not improve from 0.85789
Epoch 59/500
2660/2660 [==============================] - 2s 935us/step - loss: 0.5632 - acc: 0.8523 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00059: val_acc did not improve from 0.85789
Epoch 60/500
2660/2660 [==============================] - 2s 931us/step - loss: 0.5225 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00060: val_acc did not improve from 0.85789
Epoch 61/500
2660/2660 [==============================] - 2s 935us/step - loss: 0.5565 - acc: 0.8530 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00061: ReduceLROnPlateau reducing learning rate to 2.000000303716519e-15.

Epoch 00061: val_acc did not improve from 0.85789
Epoch 62/500
2660/2660 [==============================] - 2s 927us/step - loss: 0.5534 - acc: 0.8470 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00062: val_acc did not improve from 0.85789
Epoch 63/500
2660/2660 [==============================] - 2s 924us/step - loss: 0.5479 - acc: 0.8477 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00063: val_acc did not improve from 0.85789
Epoch 64/500
2660/2660 [==============================] - 2s 923us/step - loss: 0.5292 - acc: 0.8526 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00064: val_acc did not improve from 0.85789
Epoch 65/500
2660/2660 [==============================] - 2s 922us/step - loss: 0.5189 - acc: 0.8658 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00065: val_acc did not improve from 0.85789
Epoch 66/500
2660/2660 [==============================] - 2s 920us/step - loss: 0.5443 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00066: ReduceLROnPlateau reducing learning rate to 2.0000002190132243e-16.

Epoch 00066: val_acc did not improve from 0.85789
Epoch 67/500
2660/2660 [==============================] - 2s 920us/step - loss: 0.5621 - acc: 0.8425 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00067: val_acc did not improve from 0.85789
Epoch 68/500
2660/2660 [==============================] - 2s 917us/step - loss: 0.5185 - acc: 0.8451 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00068: val_acc did not improve from 0.85789
Epoch 69/500
2660/2660 [==============================] - 2s 915us/step - loss: 0.5744 - acc: 0.8444 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00108: val_acc did not improve from 0.85789
Epoch 109/500
2660/2660 [==============================] - 2s 911us/step - loss: 0.5310 - acc: 0.8496 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00109: val_acc did not improve from 0.85789
Epoch 110/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.4942 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00110: val_acc did not improve from 0.85789
Epoch 111/500
2660/2660 [==============================] - 2s 914us/step - loss: 0.5106 - acc: 0.8481 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00111: ReduceLROnPlateau reducing learning rate to 2.0000002362981892e-25.

Epoch 00111: val_acc did not improve from 0.85789
Epoch 112/500
2660/2660 [==============================] - 2s 915us/step - loss: 0.5191 - acc: 0.8508 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00112: val_acc did not improve from 0.85789
Epoch 113/500
2660/2660 [==============================] - 2s 912us/step - loss: 0.5474 - acc: 0.8594 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00113: val_acc did not improve from 0.85789
Epoch 114/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5541 - acc: 0.8541 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00114: val_acc did not improve from 0.85789
Epoch 115/500
2660/2660 [==============================] - 2s 915us/step - loss: 0.5431 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00115: val_acc did not improve from 0.85789
Epoch 116/500
2660/2660 [==============================] - 2s 913us/step - loss: 0.4922 - acc: 0.8560 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00116: ReduceLROnPlateau reducing learning rate to 2.0000002856019956e-26.

Epoch 00116: val_acc did not improve from 0.85789
Epoch 117/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5649 - acc: 0.8541 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00117: val_acc did not improve from 0.85789
Epoch 118/500
2660/2660 [==============================] - 2s 913us/step - loss: 0.4859 - acc: 0.8617 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00118: val_acc did not improve from 0.85789
Epoch 119/500
2660/2660 [==============================] - 2s 920us/step - loss: 0.6096 - acc: 0.8451 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00119: val_acc did not improve from 0.85789
Epoch 120/500
2660/2660 [==============================] - 2s 912us/step - loss: 0.5509 - acc: 0.8474 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00120: val_acc did not improve from 0.85789
Epoch 121/500
2660/2660 [==============================] - 2s 912us/step - loss: 0.5289 - acc: 0.8553 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00121: ReduceLROnPlateau reducing learning rate to 2.000000285601996e-27.

Epoch 00121: val_acc did not improve from 0.85789
Epoch 122/500
2660/2660 [==============================] - 2s 911us/step - loss: 0.5482 - acc: 0.8549 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00122: val_acc did not improve from 0.85789
Epoch 123/500
2660/2660 [==============================] - 2s 913us/step - loss: 0.6003 - acc: 0.8451 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00123: val_acc did not improve from 0.85789
Epoch 124/500
2660/2660 [==============================] - 2s 916us/step - loss: 0.5770 - acc: 0.8477 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00124: val_acc did not improve from 0.85789
Epoch 125/500
2660/2660 [==============================] - 2s 912us/step - loss: 0.5179 - acc: 0.8549 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00125: val_acc did not improve from 0.85789
Epoch 126/500
2660/2660 [==============================] - 2s 918us/step - loss: 0.5478 - acc: 0.8451 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00126: ReduceLROnPlateau reducing learning rate to 2.0000002470833968e-28.

Epoch 00126: val_acc did not improve from 0.85789
Epoch 127/500
2660/2660 [==============================] - 3s 964us/step - loss: 0.4957 - acc: 0.8553 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00127: val_acc did not improve from 0.85789
Epoch 128/500
2660/2660 [==============================] - 3s 966us/step - loss: 0.5311 - acc: 0.8538 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00128: val_acc did not improve from 0.85789
Epoch 129/500
2660/2660 [==============================] - 3s 957us/step - loss: 0.4982 - acc: 0.8598 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00129: val_acc did not improve from 0.85789
Epoch 130/500
2660/2660 [==============================] - 3s 947us/step - loss: 0.5219 - acc: 0.8455 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00130: val_acc did not improve from 0.85789
Epoch 131/500
2660/2660 [==============================] - 3s 946us/step - loss: 0.5765 - acc: 0.8451 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00131: ReduceLROnPlateau reducing learning rate to 2.000000247083397e-29.

Epoch 00131: val_acc did not improve from 0.85789
Epoch 132/500
2660/2660 [==============================] - 3s 941us/step - loss: 0.5290 - acc: 0.8508 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00132: val_acc did not improve from 0.85789
Epoch 133/500
2660/2660 [==============================] - 2s 933us/step - loss: 0.5343 - acc: 0.8568 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00133: val_acc did not improve from 0.85789
Epoch 134/500
2660/2660 [==============================] - 2s 933us/step - loss: 0.5333 - acc: 0.8538 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00134: val_acc did not improve from 0.85789
Epoch 135/500
2660/2660 [==============================] - 2s 934us/step - loss: 0.5600 - acc: 0.8477 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00135: val_acc did not improve from 0.85789
Epoch 136/500
2660/2660 [==============================] - 2s 925us/step - loss: 0.5413 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00136: ReduceLROnPlateau reducing learning rate to 2.0000003072687078e-30.

Epoch 00136: val_acc did not improve from 0.85789
Epoch 137/500
2660/2660 [==============================] - 2s 924us/step - loss: 0.5478 - acc: 0.8470 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00137: val_acc did not improve from 0.85789
Epoch 138/500
2660/2660 [==============================] - 2s 924us/step - loss: 0.5027 - acc: 0.8579 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00138: val_acc did not improve from 0.85789
Epoch 139/500
2660/2660 [==============================] - 2s 922us/step - loss: 0.5657 - acc: 0.8398 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00139: val_acc did not improve from 0.85789
Epoch 140/500
2660/2660 [==============================] - 2s 921us/step - loss: 0.5822 - acc: 0.8504 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00140: val_acc did not improve from 0.85789
Epoch 141/500
2660/2660 [==============================] - 2s 918us/step - loss: 0.5448 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00141: ReduceLROnPlateau reducing learning rate to 2.000000382500346e-31.

Epoch 00141: val_acc did not improve from 0.85789
Epoch 142/500
2660/2660 [==============================] - 2s 922us/step - loss: 0.5287 - acc: 0.8474 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00142: val_acc did not improve from 0.85789
Epoch 143/500
2660/2660 [==============================] - 2s 915us/step - loss: 0.5774 - acc: 0.8429 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00143: val_acc did not improve from 0.85789
Epoch 144/500
2660/2660 [==============================] - 2s 917us/step - loss: 0.5995 - acc: 0.8436 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00144: val_acc did not improve from 0.85789
Epoch 145/500
2660/2660 [==============================] - 2s 915us/step - loss: 0.5568 - acc: 0.8496 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00145: val_acc did not improve from 0.85789
Epoch 146/500
2660/2660 [==============================] - 2s 914us/step - loss: 0.5683 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00146: ReduceLROnPlateau reducing learning rate to 2.0000004295201202e-32.

Epoch 00146: val_acc did not improve from 0.85789
Epoch 147/500
2660/2660 [==============================] - 2s 913us/step - loss: 0.5838 - acc: 0.8511 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00147: val_acc did not improve from 0.85789
Epoch 148/500
2660/2660 [==============================] - 2s 913us/step - loss: 0.4909 - acc: 0.8511 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00148: val_acc did not improve from 0.85789
Epoch 149/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5371 - acc: 0.8515 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00149: val_acc did not improve from 0.85789
Epoch 150/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5497 - acc: 0.8462 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00150: val_acc did not improve from 0.85789
Epoch 151/500
2660/2660 [==============================] - 2s 911us/step - loss: 0.5448 - acc: 0.8376 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00151: ReduceLROnPlateau reducing learning rate to 2.0000004882948376e-33.

Epoch 00151: val_acc did not improve from 0.85789
Epoch 152/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5277 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00152: val_acc did not improve from 0.85789
Epoch 153/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5754 - acc: 0.8402 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00153: val_acc did not improve from 0.85789
Epoch 154/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.4831 - acc: 0.8519 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00154: val_acc did not improve from 0.85789
Epoch 155/500
2660/2660 [==============================] - 2s 906us/step - loss: 0.6051 - acc: 0.8402 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00155: val_acc did not improve from 0.85789
Epoch 156/500
2660/2660 [==============================] - 2s 904us/step - loss: 0.5019 - acc: 0.8530 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00156: ReduceLROnPlateau reducing learning rate to 2.0000004148264407e-34.

Epoch 00156: val_acc did not improve from 0.85789
Epoch 157/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5291 - acc: 0.8496 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00157: val_acc did not improve from 0.85789
Epoch 158/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5106 - acc: 0.8620 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00158: val_acc did not improve from 0.85789
Epoch 159/500
2660/2660 [==============================] - 2s 906us/step - loss: 0.5123 - acc: 0.8470 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00159: val_acc did not improve from 0.85789
Epoch 160/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5790 - acc: 0.8383 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00160: val_acc did not improve from 0.85789
Epoch 161/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.5944 - acc: 0.8466 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00161: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-35.

Epoch 00161: val_acc did not improve from 0.85789
Epoch 162/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.5315 - acc: 0.8575 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00162: val_acc did not improve from 0.85789
Epoch 163/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.5166 - acc: 0.8590 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00163: val_acc did not improve from 0.85789
Epoch 164/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.4863 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00164: val_acc did not improve from 0.85789
Epoch 165/500
2660/2660 [==============================] - 2s 904us/step - loss: 0.5309 - acc: 0.8444 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00165: val_acc did not improve from 0.85789
Epoch 166/500
2660/2660 [==============================] - 2s 901us/step - loss: 0.5248 - acc: 0.8462 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00166: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-36.

Epoch 00166: val_acc did not improve from 0.85789
Epoch 167/500
2660/2660 [==============================] - 2s 901us/step - loss: 0.5492 - acc: 0.8545 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00167: val_acc did not improve from 0.85789
Epoch 168/500
2660/2660 [==============================] - 2s 904us/step - loss: 0.5754 - acc: 0.8432 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00168: val_acc did not improve from 0.85789
Epoch 169/500
2660/2660 [==============================] - 2s 905us/step - loss: 0.5302 - acc: 0.8500 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00169: val_acc did not improve from 0.85789
Epoch 170/500
2660/2660 [==============================] - 2s 904us/step - loss: 0.5318 - acc: 0.8553 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00170: val_acc did not improve from 0.85789
Epoch 171/500
2660/2660 [==============================] - 2s 902us/step - loss: 0.5397 - acc: 0.8515 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00171: ReduceLROnPlateau reducing learning rate to 2.0000002512444633e-37.

Epoch 00171: val_acc did not improve from 0.85789
Epoch 172/500
2660/2660 [==============================] - 2s 902us/step - loss: 0.5032 - acc: 0.8511 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00172: val_acc did not improve from 0.85789
Epoch 173/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.4960 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00173: val_acc did not improve from 0.85789
Epoch 174/500
2660/2660 [==============================] - 2s 902us/step - loss: 0.5531 - acc: 0.8474 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00174: val_acc did not improve from 0.85789
Epoch 175/500
2660/2660 [==============================] - 2s 903us/step - loss: 0.4824 - acc: 0.8530 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00175: val_acc did not improve from 0.85789
Epoch 176/500
2660/2660 [==============================] - 2s 907us/step - loss: 0.5340 - acc: 0.8462 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00176: ReduceLROnPlateau reducing learning rate to 2.0000002064029123e-38.

Epoch 00176: val_acc did not improve from 0.85789
Epoch 177/500
2660/2660 [==============================] - 2s 907us/step - loss: 0.5473 - acc: 0.8496 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00177: val_acc did not improve from 0.85789
Epoch 178/500
1680/2660 [=================>............] - ETA: 0s - loss: 0.5777 - acc: 0.8476

IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.

Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)

2660/2660 [==============================] - 2s 921us/step - loss: 0.5263 - acc: 0.8481 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00201: ReduceLROnPlateau reducing learning rate to 1.9996529085915142e-43.

Epoch 00201: val_acc did not improve from 0.85789
Epoch 202/500
2660/2660 [==============================] - 2s 911us/step - loss: 0.5118 - acc: 0.8556 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00202: val_acc did not improve from 0.85789
Epoch 203/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5742 - acc: 0.8523 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00203: val_acc did not improve from 0.85789
Epoch 204/500
2660/2660 [==============================] - 2s 909us/step - loss: 0.5323 - acc: 0.8485 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00204: val_acc did not improve from 0.85789
Epoch 205/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5631 - acc: 0.8402 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00205: val_acc did not improve from 0.85789
Epoch 206/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5282 - acc: 0.8541 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00206: ReduceLROnPlateau reducing learning rate to 2.0038568039844885e-44.

Epoch 00206: val_acc did not improve from 0.85789
Epoch 207/500
2660/2660 [==============================] - 2s 908us/step - loss: 0.5462 - acc: 0.8444 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00207: val_acc did not improve from 0.85789
Epoch 208/500
2660/2660 [==============================] - 2s 910us/step - loss: 0.5333 - acc: 0.8526 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00208: val_acc did not improve from 0.85789
Epoch 209/500
2660/2660 [==============================] - 2s 911us/step - loss: 0.5293 - acc: 0.8534 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00209: val_acc did not improve from 0.85789
Epoch 210/500
2660/2660 [==============================] - 2s 916us/step - loss: 0.5427 - acc: 0.8511 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00210: val_acc did not improve from 0.85789
Epoch 211/500
2660/2660 [==============================] - 2s 917us/step - loss: 0.4599 - acc: 0.8624 - val_loss: 0.5666 - val_acc: 0.8553

Epoch 00211: ReduceLROnPlateau reducing learning rate to 1.961817850054744e-45.

Epoch 00211: val_acc did not improve from 0.85789
Epoch 212/500
 592/2660 [=====>........................] - ETA: 1s - loss: 0.6624 - acc: 0.8530

4.2.2 VGG Model

4.2.2.1 Prepare & Load Data

The VGG model input dimesions are 224 * 224 * 3. Hence we need to prepare images satisfying the dimensional requirements.

images_vgg = np.array([])

try:
    print('Loading VGG Images & Labels dict ...')
    images_vgg = joblib.load(os.path.join(
        './save/pickles', 'images-vgg.pickle'))
    labels_vgg = joblib.load(os.path.join(
        './save/pickles', 'labels-vgg.pickle'))
    print('Loading done.')
except Exception as e:
    print('Stacktrace', e)
    print('VGG Images & Labels dict does not exist.')

if (not images_vgg.size or not labels_vgg.size):
    img_size = (224, 224)
    print('Running load_seedlings method with img_size {} ...'.format(img_size))
    images_vgg, labels_vgg, images_per_class = load_seedlings_transfer(
        img_size=img_size)

    print('Dumping VGG Images & Labels dict ...')
    # Dumping dicts
    joblib.dump(images_vgg, os.path.join(
        './save/pickles', 'images-vgg.pickle'))
    joblib.dump(labels_vgg, os.path.join(
        './save/pickles', 'labels-vgg.pickle'))
    print('VGG Images & Labels dict dumping done.')

Loading VGG Images, Labels & images_per_class dict ...
Stacktrace [Errno 2] No such file or directory: './save/pickles/images-vgg.pickle'
VGG Images, Labels or images_per_class dict does not exist.
Running load_seedlings method with img_size (224, 224) ...
Dumping VGG Images, Labels & images_per_class dict ...
VGG Images, Labels or images_per_class dict dumping done.

# Type cast int to float for VGG16 model compatibility

images_vgg = images_vgg.astype(dtype='float32', copy=False)

print('Images Shape:', images_vgg.shape)
print('Image Data Type:', images_vgg[0].dtype)
print('Labels Shape:', labels_vgg.shape)
print('Label Data Type:', labels_vgg[0].dtype)

Images Shape: (4750, 224, 224, 3)
Image Data Type: float32
Labels Shape: (4750,)
Label Data Type: <U10

4.2.2.2 Extract VGG Bottleneck Features

%time

input_dim = 224
POOLING = 'avg'
RANDOM_STATE = 21

# Numpy array to store extracted features from VGG model
images_vgg_features = np.zeros(
    (len(images_vgg), input_dim, input_dim, 3), dtype='float32')

# Extract features with Xception model
for i, image in tqdm(enumerate(images_vgg)):
    images_vgg_features[i] = vgg16.preprocess_input(
        np.expand_dims(image, axis=0))

print('Train Images shape: {} size: {:,}'.format(
    images_vgg_features.shape, images_vgg_features.size))

109it [00:00, 1087.22it/s]

CPU times: user 0 ns, sys: 10 µs, total: 10 µs
Wall time: 19.1 µs

4750it [00:02, 1954.69it/s]

Train Images shape: (4750, 224, 224, 3) size: 715,008,000

# Split the data in train & validation set
x_train, x_valid, y_train, y_valid = train_test_split(images_vgg_features,
                                                      labels_vgg,
                                                      shuffle=True,
                                                      train_size=0.8,
                                                      stratify=labels_vgg,
                                                      random_state=RANDOM_STATE
                                                      )

del images_vgg_features, labels_vgg

# VGG16 model, with weights pre-trained on ImageNet. This model can be built both with
# 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).
# The default input size for this model is 224x224.

vgg_bottleneck = vgg16.VGG16(
    weights='imagenet', include_top=False, pooling=POOLING)

# Calculating VGG train & valid set bottleneck features
x_train_botf_vgg = vgg_bottleneck.predict(x_train, batch_size=32, verbose=1)
x_valid_botf_vgg = vgg_bottleneck.predict(x_valid, batch_size=32, verbose=1)

del x_train, x_valid

print('VGG train bottleneck features shape: {} size: {:,}'.format(
    x_train_botf_vgg.shape, x_train_botf_vgg.size))
print('VGG valid bottleneck features shape: {} size: {:,}'.format(
    x_valid_botf_vgg.shape, x_valid_botf_vgg.size))

# Dumping VGG train & valid set bottleneck features
joblib.dump(x_train_botf_vgg, os.path.join(
    './save/pickles', 'x_train_botf_vgg.pickle'))
joblib.dump(x_valid_botf_vgg, os.path.join(
    './save/pickles', 'x_valid_botf_vgg.pickle'))

joblib.dump(y_train, os.path.join('./save/pickles', 'y_train_botf_vgg.pickle'))
joblib.dump(y_valid, os.path.join('./save/pickles', 'y_valid_botf_vgg.pickle'))
print('Dumping VGG train & valid set bottleneck features & labels done.')

3800/3800 [==============================] - 74s 20ms/step
950/950 [==============================] - 13s 14ms/step
VGG train bottleneck features shape: (3800, 512) size: 1,945,600
VGG valid bottleneck features shape: (950, 512) size: 486,400
Dumping VGG train & valid set bottleneck features & labels done.

4.2.2.3 Training a Keras Dense Model

print('Loading VGG Images, Labels dict ...')
x_train_botf_vgg = joblib.load(os.path.join(
    './save/pickles', 'x_train_botf_vgg.pickle'))
x_valid_botf_vgg = joblib.load(os.path.join(
    './save/pickles', 'x_valid_botf_vgg.pickle'))

y_train = joblib.load(os.path.join(
    './save/pickles', 'y_train_botf_vgg.pickle'))
y_valid = joblib.load(os.path.join(
    './save/pickles', 'y_valid_botf_vgg.pickle'))
print('Loading VGG train & valid set bottleneck features & labels done.')

Loading VGG Images, Labels & images_per_class dict ...
Loading VGG train & valid set bottleneck features & labels done.

# Define Model Neural Network Architecture


def model_neural_network_vgg():

    # Model Neural Network Architecture
    inputs = Input(shape=(512,))

    # Adding 1st Fully connected, output layer & Dropout
    fc = Dense(input_shape=(512,), units=512, activation='relu')(inputs)
    fc = Dropout(0.3)(fc)

    # Adding 2nd Fully connected, output layer & Dropout
    fc = Dense(input_shape=(256,), units=256, activation='relu')(fc)
    fc = Dropout(0.2)(fc)

    # Adding 3rd Fully connected, output layer & Dropout
    fc = Dense(input_shape=(128,), units=128, activation='relu')(fc)
    fc = Dropout(0.1)(fc)

    # Adding output layer
    outputs = Dense(units=12, activation='softmax')(fc)

    model = Model(inputs=inputs, outputs=outputs)

    mypotim = Adam(lr=2 * 1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
    #mypotim = SGD(lr=1 * 1e-1, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy',
                  optimizer=mypotim,
                  metrics=['accuracy'])
    # Model Summary
    # model.summary()

    return model

% % time

BATCH_SIZE = 16
EPOCHS = 500
RANDOM_STATE = 21
# Fixing random seed
np.random.seed(RANDOM_STATE)

# Reduce LR On Plateau
lr_reduced = ReduceLROnPlateau(
    monitor='val_acc', factor=0.1, epsilon=1e-5, patience=5, verbose=1)

# Setting checkpoint & early stopping
checkpoint_path = './checkpoint/VGG_ADAM_best_weights.{epoch:02d}-{acc:.4f}.hdf5'
checkpoint = ModelCheckpoint(
    checkpoint_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')

# TensorBoard logs
tensorboard = TensorBoard(
    log_dir='./logs', histogram_freq=2000, write_graph=True, write_images=True)

# Early Stopping
# early_stop = EarlyStopping(monitor='val_acc', patience=5, mode='max')

# All callbacks
callbacks_list = [lr_reduced, checkpoint, tensorboard]


# Model Neural Network Architecture
inputs = Input(shape=(512,))

# Adding 1st Fully connected, output layer & Dropout
fc = Dense(input_shape=(512,), units=512, activation='relu')(inputs)
fc = Dropout(0.3)(fc)

# Adding 2nd Fully connected, output layer & Dropout
fc = Dense(input_shape=(256,), units=256, activation='relu')(fc)
fc = Dropout(0.2)(fc)

# Adding 3rd Fully connected, output layer & Dropout
fc = Dense(input_shape=(128,), units=128, activation='relu')(fc)
fc = Dropout(0.1)(fc)

# Adding output layer
outputs = Dense(units=12, activation='softmax')(fc)

model = Model(inputs=inputs, outputs=outputs)


best_model = 'VGG_ADAM_best_weights.24-0.9872.hdf5'
# Load network weights
if os.path.exists(os.path.join('./checkpoint', best_model)):
    print('Loading weights from previous run...', best_model)
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))
else:
    print('Weights not loaded.', best_model)


mypotim = Adam(lr=2 * 1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
#mypotim = SGD(lr=1 * 1e-1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=mypotim,
              metrics=['accuracy'])
# Model Summary
model.summary()

# Convert categorical labels into one-hot vector
y_train = to_categorical(np.array([CLASS[l] for l in y_train]))

# Fit the model
model.fit(x_train_botf_vgg, y_train, validation_split=0.3, batch_size=BATCH_SIZE,
          epochs=EPOCHS, verbose=0, shuffle=True, callbacks=callbacks_list)

/home/indiano/miniconda3/envs/tf/lib/python3.5/site-packages/keras/callbacks.py:999: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.
  warnings.warn('`epsilon` argument is deprecated and '

Loading weights from previous run... VGG_ADAM_best_weights.24-0.9872.hdf5
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               262656    
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 256)               131328    
_________________________________________________________________
dropout_2 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 128)               32896     
_________________________________________________________________
dropout_3 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 12)                1548      
=================================================================
Total params: 428,428
Trainable params: 428,428
Non-trainable params: 0
_________________________________________________________________

Epoch 00001: val_acc improved from -inf to 0.84035, saving model to ./checkpoint/VGG_ADAM_best_weights.01-0.9447.hdf5

Epoch 00002: val_acc did not improve from 0.84035

Epoch 00003: val_acc did not improve from 0.84035

Epoch 00004: val_acc improved from 0.84035 to 0.84561, saving model to ./checkpoint/VGG_ADAM_best_weights.04-0.9316.hdf5

Epoch 00005: val_acc did not improve from 0.84561

Epoch 00006: val_acc did not improve from 0.84561

Epoch 00007: val_acc did not improve from 0.84561

Epoch 00008: val_acc did not improve from 0.84561

Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.

Epoch 00009: val_acc did not improve from 0.84561

Epoch 00010: val_acc improved from 0.84561 to 0.86140, saving model to ./checkpoint/VGG_ADAM_best_weights.10-0.9602.hdf5

Epoch 00011: val_acc improved from 0.86140 to 0.86491, saving model to ./checkpoint/VGG_ADAM_best_weights.11-0.9741.hdf5

Epoch 00012: val_acc improved from 0.86491 to 0.86754, saving model to ./checkpoint/VGG_ADAM_best_weights.12-0.9805.hdf5

Epoch 00013: val_acc improved from 0.86754 to 0.86842, saving model to ./checkpoint/VGG_ADAM_best_weights.13-0.9786.hdf5

Epoch 00014: val_acc did not improve from 0.86842

Epoch 00015: val_acc did not improve from 0.86842

Epoch 00016: val_acc did not improve from 0.86842

Epoch 00017: val_acc did not improve from 0.86842

Epoch 00018: ReduceLROnPlateau reducing learning rate to 2.0000000949949027e-05.

Epoch 00018: val_acc did not improve from 0.86842

Epoch 00019: val_acc did not improve from 0.86842

Epoch 00020: val_acc did not improve from 0.86842

Epoch 00021: val_acc did not improve from 0.86842

Epoch 00022: val_acc did not improve from 0.86842

Epoch 00023: ReduceLROnPlateau reducing learning rate to 2.0000001313746906e-06.

Epoch 00023: val_acc did not improve from 0.86842

Epoch 00024: val_acc did not improve from 0.86842

Epoch 00025: val_acc did not improve from 0.86842

Epoch 00026: val_acc did not improve from 0.86842

Epoch 00027: val_acc did not improve from 0.86842

Epoch 00028: ReduceLROnPlateau reducing learning rate to 2.000000222324161e-07.

Epoch 00028: val_acc did not improve from 0.86842

Epoch 00029: val_acc did not improve from 0.86842

Epoch 00030: val_acc did not improve from 0.86842

Epoch 00031: val_acc did not improve from 0.86842

Epoch 00032: val_acc did not improve from 0.86842

Epoch 00033: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-08.

Epoch 00033: val_acc did not improve from 0.86842

Epoch 00034: val_acc did not improve from 0.86842

Epoch 00035: val_acc did not improve from 0.86842

Epoch 00036: val_acc did not improve from 0.86842

Epoch 00037: val_acc did not improve from 0.86842

Epoch 00038: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-09.

Epoch 00038: val_acc did not improve from 0.86842

Epoch 00039: val_acc did not improve from 0.86842

Epoch 00040: val_acc did not improve from 0.86842

Epoch 00041: val_acc did not improve from 0.86842

Epoch 00042: val_acc did not improve from 0.86842

Epoch 00043: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-10.

Epoch 00043: val_acc did not improve from 0.86842

Epoch 00044: val_acc did not improve from 0.86842

Epoch 00045: val_acc did not improve from 0.86842

Epoch 00046: val_acc did not improve from 0.86842

Epoch 00047: val_acc did not improve from 0.86842

Epoch 00048: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-11.

Epoch 00048: val_acc did not improve from 0.86842

Epoch 00049: val_acc did not improve from 0.86842

Epoch 00050: val_acc did not improve from 0.86842

Epoch 00051: val_acc did not improve from 0.86842

Epoch 00052: val_acc did not improve from 0.86842

Epoch 00053: ReduceLROnPlateau reducing learning rate to 2.000000165480742e-12.

Epoch 00053: val_acc did not improve from 0.86842

Epoch 00054: val_acc did not improve from 0.86842

Epoch 00055: val_acc did not improve from 0.86842

Epoch 00056: val_acc did not improve from 0.86842

Epoch 00057: val_acc did not improve from 0.86842

Epoch 00058: ReduceLROnPlateau reducing learning rate to 2.000000208848829e-13.

Epoch 00058: val_acc did not improve from 0.86842

Epoch 00059: val_acc did not improve from 0.86842

Epoch 00060: val_acc did not improve from 0.86842

Epoch 00061: val_acc did not improve from 0.86842

Epoch 00062: val_acc did not improve from 0.86842

Epoch 00063: ReduceLROnPlateau reducing learning rate to 2.0000002359538835e-14.

Epoch 00063: val_acc did not improve from 0.86842

Epoch 00064: val_acc did not improve from 0.86842

Epoch 00065: val_acc did not improve from 0.86842

Epoch 00066: val_acc did not improve from 0.86842

Epoch 00067: val_acc did not improve from 0.86842

Epoch 00068: ReduceLROnPlateau reducing learning rate to 2.000000303716519e-15.

Epoch 00068: val_acc did not improve from 0.86842

Epoch 00069: val_acc did not improve from 0.86842

Epoch 00070: val_acc did not improve from 0.86842

Epoch 00071: val_acc did not improve from 0.86842

Epoch 00072: val_acc did not improve from 0.86842

Epoch 00073: ReduceLROnPlateau reducing learning rate to 2.0000002190132243e-16.

Epoch 00073: val_acc did not improve from 0.86842

Epoch 00074: val_acc did not improve from 0.86842

Epoch 00075: val_acc did not improve from 0.86842

Epoch 00076: val_acc did not improve from 0.86842

Epoch 00077: val_acc did not improve from 0.86842

Epoch 00078: ReduceLROnPlateau reducing learning rate to 2.0000001660736652e-17.

Epoch 00078: val_acc did not improve from 0.86842

Epoch 00079: val_acc did not improve from 0.86842

Epoch 00080: val_acc did not improve from 0.86842

Epoch 00081: val_acc did not improve from 0.86842

Epoch 00082: val_acc did not improve from 0.86842

Epoch 00083: ReduceLROnPlateau reducing learning rate to 2.0000001329864408e-18.

Epoch 00083: val_acc did not improve from 0.86842

Epoch 00084: val_acc did not improve from 0.86842

Epoch 00085: val_acc did not improve from 0.86842

Epoch 00086: val_acc did not improve from 0.86842

Epoch 00087: val_acc did not improve from 0.86842

Epoch 00088: ReduceLROnPlateau reducing learning rate to 2.00000009162741e-19.

Epoch 00088: val_acc did not improve from 0.86842

Epoch 00089: val_acc did not improve from 0.86842

Epoch 00090: val_acc did not improve from 0.86842

Epoch 00091: val_acc did not improve from 0.86842

Epoch 00092: val_acc did not improve from 0.86842

Epoch 00093: ReduceLROnPlateau reducing learning rate to 2.000000065778016e-20.

Epoch 00093: val_acc did not improve from 0.86842

Epoch 00094: val_acc did not improve from 0.86842

Epoch 00095: val_acc did not improve from 0.86842

Epoch 00096: val_acc did not improve from 0.86842

Epoch 00097: val_acc did not improve from 0.86842

Epoch 00098: ReduceLROnPlateau reducing learning rate to 2.0000000980897586e-21.

Epoch 00098: val_acc did not improve from 0.86842

Epoch 00099: val_acc did not improve from 0.86842

Epoch 00100: val_acc did not improve from 0.86842

Epoch 00101: val_acc did not improve from 0.86842

Epoch 00102: val_acc did not improve from 0.86842

Epoch 00103: ReduceLROnPlateau reducing learning rate to 2.000000138479437e-22.

Epoch 00103: val_acc did not improve from 0.86842

Epoch 00104: val_acc did not improve from 0.86842

Epoch 00105: val_acc did not improve from 0.86842

Epoch 00106: val_acc did not improve from 0.86842

Epoch 00107: val_acc did not improve from 0.86842

Epoch 00108: ReduceLROnPlateau reducing learning rate to 2.000000188966535e-23.

Epoch 00108: val_acc did not improve from 0.86842

Epoch 00109: val_acc did not improve from 0.86842

Epoch 00110: val_acc did not improve from 0.86842

Epoch 00111: val_acc did not improve from 0.86842

Epoch 00112: val_acc did not improve from 0.86842

Epoch 00113: ReduceLROnPlateau reducing learning rate to 2.000000157412099e-24.

Epoch 00113: val_acc did not improve from 0.86842

Epoch 00114: val_acc did not improve from 0.86842

Epoch 00115: val_acc did not improve from 0.86842

Epoch 00116: val_acc did not improve from 0.86842

Epoch 00117: val_acc did not improve from 0.86842

Epoch 00118: ReduceLROnPlateau reducing learning rate to 2.0000002362981892e-25.

Epoch 00118: val_acc did not improve from 0.86842

Epoch 00119: val_acc did not improve from 0.86842

Epoch 00120: val_acc did not improve from 0.86842

Epoch 00121: val_acc did not improve from 0.86842

Epoch 00122: val_acc did not improve from 0.86842

Epoch 00123: ReduceLROnPlateau reducing learning rate to 2.0000002856019956e-26.

Epoch 00123: val_acc did not improve from 0.86842

Epoch 00124: val_acc did not improve from 0.86842

Epoch 00125: val_acc did not improve from 0.86842

Epoch 00126: val_acc did not improve from 0.86842

Epoch 00127: val_acc did not improve from 0.86842

Epoch 00128: ReduceLROnPlateau reducing learning rate to 2.000000285601996e-27.

Epoch 00128: val_acc did not improve from 0.86842

Epoch 00129: val_acc did not improve from 0.86842

Epoch 00130: val_acc did not improve from 0.86842

Epoch 00131: val_acc did not improve from 0.86842

Epoch 00132: val_acc did not improve from 0.86842

Epoch 00133: ReduceLROnPlateau reducing learning rate to 2.0000002470833968e-28.

Epoch 00133: val_acc did not improve from 0.86842

Epoch 00134: val_acc did not improve from 0.86842

Epoch 00135: val_acc did not improve from 0.86842

Epoch 00136: val_acc did not improve from 0.86842

Epoch 00137: val_acc did not improve from 0.86842

Epoch 00138: ReduceLROnPlateau reducing learning rate to 2.000000247083397e-29.

Epoch 00138: val_acc did not improve from 0.86842

Epoch 00139: val_acc did not improve from 0.86842

Epoch 00140: val_acc did not improve from 0.86842

Epoch 00141: val_acc did not improve from 0.86842

Epoch 00142: val_acc did not improve from 0.86842

Epoch 00143: ReduceLROnPlateau reducing learning rate to 2.0000003072687078e-30.

Epoch 00143: val_acc did not improve from 0.86842

Epoch 00144: val_acc did not improve from 0.86842

Epoch 00145: val_acc did not improve from 0.86842

Epoch 00146: val_acc did not improve from 0.86842

Epoch 00147: val_acc did not improve from 0.86842

Epoch 00148: ReduceLROnPlateau reducing learning rate to 2.000000382500346e-31.

Epoch 00148: val_acc did not improve from 0.86842

Epoch 00149: val_acc did not improve from 0.86842

Epoch 00150: val_acc did not improve from 0.86842

Epoch 00151: val_acc did not improve from 0.86842

Epoch 00152: val_acc did not improve from 0.86842

Epoch 00153: ReduceLROnPlateau reducing learning rate to 2.0000004295201202e-32.

Epoch 00153: val_acc did not improve from 0.86842

Epoch 00154: val_acc did not improve from 0.86842

Epoch 00155: val_acc did not improve from 0.86842

Epoch 00156: val_acc did not improve from 0.86842

Epoch 00157: val_acc did not improve from 0.86842

Epoch 00158: ReduceLROnPlateau reducing learning rate to 2.0000004882948376e-33.

Epoch 00158: val_acc did not improve from 0.86842

Epoch 00159: val_acc did not improve from 0.86842

Epoch 00160: val_acc did not improve from 0.86842

Epoch 00161: val_acc did not improve from 0.86842

Epoch 00162: val_acc did not improve from 0.86842

Epoch 00163: ReduceLROnPlateau reducing learning rate to 2.0000004148264407e-34.

Epoch 00163: val_acc did not improve from 0.86842

Epoch 00164: val_acc did not improve from 0.86842

Epoch 00165: val_acc did not improve from 0.86842

Epoch 00166: val_acc did not improve from 0.86842

Epoch 00167: val_acc did not improve from 0.86842

Epoch 00168: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-35.

Epoch 00168: val_acc did not improve from 0.86842

Epoch 00169: val_acc did not improve from 0.86842

Epoch 00170: val_acc did not improve from 0.86842

Epoch 00171: val_acc did not improve from 0.86842

Epoch 00172: val_acc did not improve from 0.86842

Epoch 00173: ReduceLROnPlateau reducing learning rate to 2.0000003229909445e-36.

Epoch 00173: val_acc did not improve from 0.86842

Epoch 00174: val_acc did not improve from 0.86842

Epoch 00175: val_acc did not improve from 0.86842

Epoch 00176: val_acc did not improve from 0.86842

Epoch 00177: val_acc did not improve from 0.86842

Epoch 00178: ReduceLROnPlateau reducing learning rate to 2.0000002512444633e-37.

Epoch 00178: val_acc did not improve from 0.86842

Epoch 00179: val_acc did not improve from 0.86842

Epoch 00180: val_acc did not improve from 0.86842

Epoch 00181: val_acc did not improve from 0.86842

Epoch 00182: val_acc did not improve from 0.86842

Epoch 00183: ReduceLROnPlateau reducing learning rate to 2.0000002064029123e-38.

Epoch 00183: val_acc did not improve from 0.86842

Epoch 00184: val_acc did not improve from 0.86842

Epoch 00185: val_acc did not improve from 0.86842

Epoch 00186: val_acc did not improve from 0.86842

Epoch 00187: val_acc did not improve from 0.86842

Epoch 00188: ReduceLROnPlateau reducing learning rate to 2.0000001503509737e-39.

Epoch 00188: val_acc did not improve from 0.86842

Epoch 00189: val_acc did not improve from 0.86842

Epoch 00190: val_acc did not improve from 0.86842

Epoch 00191: val_acc did not improve from 0.86842

Epoch 00192: val_acc did not improve from 0.86842

Epoch 00193: ReduceLROnPlateau reducing learning rate to 2.0000004306106668e-40.

Epoch 00193: val_acc did not improve from 0.86842

Epoch 00194: val_acc did not improve from 0.86842

Epoch 00195: val_acc did not improve from 0.86842

Epoch 00196: val_acc did not improve from 0.86842

Epoch 00197: val_acc did not improve from 0.86842

Epoch 00198: ReduceLROnPlateau reducing learning rate to 2.0000032332075952e-41.

Epoch 00198: val_acc did not improve from 0.86842

Epoch 00199: val_acc did not improve from 0.86842

Epoch 00200: val_acc did not improve from 0.86842

Epoch 00201: val_acc did not improve from 0.86842

Epoch 00202: val_acc did not improve from 0.86842

Epoch 00203: ReduceLROnPlateau reducing learning rate to 1.999933168284379e-42.

Epoch 00203: val_acc did not improve from 0.86842

Epoch 00204: val_acc did not improve from 0.86842

Epoch 00205: val_acc did not improve from 0.86842

Epoch 00206: val_acc did not improve from 0.86842

Epoch 00207: val_acc did not improve from 0.86842

Epoch 00208: ReduceLROnPlateau reducing learning rate to 1.9996529085915142e-43.

Epoch 00208: val_acc did not improve from 0.86842

Epoch 00209: val_acc did not improve from 0.86842

Epoch 00210: val_acc did not improve from 0.86842

Epoch 00211: val_acc did not improve from 0.86842

Epoch 00212: val_acc did not improve from 0.86842

Epoch 00213: ReduceLROnPlateau reducing learning rate to 2.0038568039844885e-44.

Epoch 00213: val_acc did not improve from 0.86842

Epoch 00214: val_acc did not improve from 0.86842

Epoch 00215: val_acc did not improve from 0.86842

Epoch 00216: val_acc did not improve from 0.86842

Epoch 00217: val_acc did not improve from 0.86842

Epoch 00218: ReduceLROnPlateau reducing learning rate to 1.961817850054744e-45.

Epoch 00218: val_acc did not improve from 0.86842

Epoch 00219: val_acc did not improve from 0.86842

Epoch 00220: val_acc did not improve from 0.86842

Epoch 00221: val_acc did not improve from 0.86842

Epoch 00222: val_acc did not improve from 0.86842

Epoch 00223: ReduceLROnPlateau reducing learning rate to 1.4012984643248171e-46.

Epoch 00223: val_acc did not improve from 0.86842

Epoch 00224: val_acc did not improve from 0.86842

Epoch 00225: val_acc did not improve from 0.86842

Epoch 00226: val_acc did not improve from 0.86842

Epoch 00227: val_acc did not improve from 0.86842

Epoch 00228: val_acc did not improve from 0.86842

Epoch 00229: val_acc did not improve from 0.86842

Epoch 00230: val_acc did not improve from 0.86842

Epoch 00231: val_acc did not improve from 0.86842

Epoch 00232: val_acc did not improve from 0.86842

Epoch 00233: val_acc did not improve from 0.86842

Epoch 00234: val_acc did not improve from 0.86842

Epoch 00235: val_acc did not improve from 0.86842

Epoch 00236: val_acc did not improve from 0.86842

Epoch 00237: val_acc did not improve from 0.86842

Epoch 00238: val_acc did not improve from 0.86842

Epoch 00239: val_acc did not improve from 0.86842

Epoch 00240: val_acc did not improve from 0.86842

Epoch 00241: val_acc did not improve from 0.86842

Epoch 00242: val_acc did not improve from 0.86842

Epoch 00243: val_acc did not improve from 0.86842

Epoch 00244: val_acc did not improve from 0.86842

Epoch 00245: val_acc did not improve from 0.86842

Epoch 00246: val_acc did not improve from 0.86842

Epoch 00247: val_acc did not improve from 0.86842

Epoch 00248: val_acc did not improve from 0.86842

Epoch 00249: val_acc did not improve from 0.86842

Epoch 00250: val_acc did not improve from 0.86842

Epoch 00251: val_acc did not improve from 0.86842

Epoch 00252: val_acc did not improve from 0.86842

Epoch 00253: val_acc did not improve from 0.86842

Epoch 00254: val_acc did not improve from 0.86842

Epoch 00255: val_acc did not improve from 0.86842

Epoch 00256: val_acc did not improve from 0.86842

Epoch 00257: val_acc did not improve from 0.86842

Epoch 00258: val_acc did not improve from 0.86842

Epoch 00259: val_acc did not improve from 0.86842

Epoch 00260: val_acc did not improve from 0.86842

Epoch 00261: val_acc did not improve from 0.86842

Epoch 00262: val_acc did not improve from 0.86842

Epoch 00263: val_acc did not improve from 0.86842

Epoch 00264: val_acc did not improve from 0.86842

Epoch 00265: val_acc did not improve from 0.86842

Epoch 00266: val_acc did not improve from 0.86842

Epoch 00267: val_acc did not improve from 0.86842

Epoch 00268: val_acc did not improve from 0.86842

Epoch 00269: val_acc did not improve from 0.86842

Epoch 00270: val_acc did not improve from 0.86842

Epoch 00271: val_acc did not improve from 0.86842

Epoch 00272: val_acc did not improve from 0.86842

Epoch 00273: val_acc did not improve from 0.86842

Epoch 00274: val_acc did not improve from 0.86842

Epoch 00275: val_acc did not improve from 0.86842

Epoch 00276: val_acc did not improve from 0.86842

Epoch 00277: val_acc did not improve from 0.86842

Epoch 00278: val_acc did not improve from 0.86842

Epoch 00279: val_acc did not improve from 0.86842

Epoch 00280: val_acc did not improve from 0.86842

Epoch 00281: val_acc did not improve from 0.86842

Epoch 00282: val_acc did not improve from 0.86842

Epoch 00283: val_acc did not improve from 0.86842

Epoch 00284: val_acc did not improve from 0.86842

Epoch 00285: val_acc did not improve from 0.86842

Epoch 00286: val_acc did not improve from 0.86842

Epoch 00287: val_acc did not improve from 0.86842

Epoch 00288: val_acc did not improve from 0.86842

Epoch 00289: val_acc did not improve from 0.86842

Epoch 00290: val_acc did not improve from 0.86842

Epoch 00291: val_acc did not improve from 0.86842

Epoch 00292: val_acc did not improve from 0.86842

Epoch 00293: val_acc did not improve from 0.86842

Epoch 00294: val_acc did not improve from 0.86842

Epoch 00295: val_acc did not improve from 0.86842

Epoch 00296: val_acc did not improve from 0.86842

Epoch 00297: val_acc did not improve from 0.86842

Epoch 00298: val_acc did not improve from 0.86842

Epoch 00299: val_acc did not improve from 0.86842

Epoch 00300: val_acc did not improve from 0.86842

Epoch 00301: val_acc did not improve from 0.86842

Epoch 00302: val_acc did not improve from 0.86842

Epoch 00303: val_acc did not improve from 0.86842

Epoch 00304: val_acc did not improve from 0.86842

Epoch 00305: val_acc did not improve from 0.86842

Epoch 00306: val_acc did not improve from 0.86842

Epoch 00307: val_acc did not improve from 0.86842

Epoch 00308: val_acc did not improve from 0.86842

Epoch 00309: val_acc did not improve from 0.86842

Epoch 00310: val_acc did not improve from 0.86842

Epoch 00311: val_acc did not improve from 0.86842

Epoch 00312: val_acc did not improve from 0.86842

Epoch 00313: val_acc did not improve from 0.86842

Epoch 00314: val_acc did not improve from 0.86842

Epoch 00315: val_acc did not improve from 0.86842

Epoch 00316: val_acc did not improve from 0.86842

Epoch 00317: val_acc did not improve from 0.86842

Epoch 00318: val_acc did not improve from 0.86842

Epoch 00319: val_acc did not improve from 0.86842

Epoch 00320: val_acc did not improve from 0.86842

Epoch 00321: val_acc did not improve from 0.86842

Epoch 00322: val_acc did not improve from 0.86842

Epoch 00323: val_acc did not improve from 0.86842

Epoch 00324: val_acc did not improve from 0.86842

Epoch 00325: val_acc did not improve from 0.86842

Epoch 00326: val_acc did not improve from 0.86842

Epoch 00327: val_acc did not improve from 0.86842

Epoch 00328: val_acc did not improve from 0.86842

Epoch 00329: val_acc did not improve from 0.86842

Epoch 00330: val_acc did not improve from 0.86842

Epoch 00331: val_acc did not improve from 0.86842

Epoch 00332: val_acc did not improve from 0.86842

Epoch 00333: val_acc did not improve from 0.86842

Epoch 00334: val_acc did not improve from 0.86842

Epoch 00335: val_acc did not improve from 0.86842

Epoch 00336: val_acc did not improve from 0.86842

Epoch 00337: val_acc did not improve from 0.86842

Epoch 00338: val_acc did not improve from 0.86842

Epoch 00339: val_acc did not improve from 0.86842

Epoch 00340: val_acc did not improve from 0.86842

Epoch 00341: val_acc did not improve from 0.86842

Epoch 00342: val_acc did not improve from 0.86842

Epoch 00343: val_acc did not improve from 0.86842

Epoch 00344: val_acc did not improve from 0.86842

Epoch 00345: val_acc did not improve from 0.86842

Epoch 00346: val_acc did not improve from 0.86842

Epoch 00347: val_acc did not improve from 0.86842

Epoch 00348: val_acc did not improve from 0.86842

Epoch 00349: val_acc did not improve from 0.86842

Epoch 00350: val_acc did not improve from 0.86842

Epoch 00351: val_acc did not improve from 0.86842

Epoch 00352: val_acc did not improve from 0.86842

Epoch 00353: val_acc did not improve from 0.86842

Epoch 00354: val_acc did not improve from 0.86842

Epoch 00355: val_acc did not improve from 0.86842

Epoch 00356: val_acc did not improve from 0.86842

Epoch 00357: val_acc did not improve from 0.86842

Epoch 00358: val_acc did not improve from 0.86842

Epoch 00359: val_acc did not improve from 0.86842

Epoch 00360: val_acc did not improve from 0.86842

Epoch 00361: val_acc did not improve from 0.86842

Epoch 00362: val_acc did not improve from 0.86842

Epoch 00363: val_acc did not improve from 0.86842

Epoch 00364: val_acc did not improve from 0.86842

Epoch 00365: val_acc did not improve from 0.86842

Epoch 00366: val_acc did not improve from 0.86842

Epoch 00367: val_acc did not improve from 0.86842

Epoch 00368: val_acc did not improve from 0.86842

Epoch 00369: val_acc did not improve from 0.86842

Epoch 00370: val_acc did not improve from 0.86842

Epoch 00371: val_acc did not improve from 0.86842

Epoch 00372: val_acc did not improve from 0.86842

Epoch 00373: val_acc did not improve from 0.86842

Epoch 00374: val_acc did not improve from 0.86842

Epoch 00375: val_acc did not improve from 0.86842

Epoch 00376: val_acc did not improve from 0.86842

Epoch 00377: val_acc did not improve from 0.86842

Epoch 00378: val_acc did not improve from 0.86842

Epoch 00379: val_acc did not improve from 0.86842

Epoch 00380: val_acc did not improve from 0.86842

Epoch 00381: val_acc did not improve from 0.86842

Epoch 00382: val_acc did not improve from 0.86842

Epoch 00383: val_acc did not improve from 0.86842

Epoch 00384: val_acc did not improve from 0.86842

Epoch 00385: val_acc did not improve from 0.86842

Epoch 00386: val_acc did not improve from 0.86842

Epoch 00387: val_acc did not improve from 0.86842

Epoch 00388: val_acc did not improve from 0.86842

Epoch 00389: val_acc did not improve from 0.86842

Epoch 00390: val_acc did not improve from 0.86842

Epoch 00391: val_acc did not improve from 0.86842

Epoch 00392: val_acc did not improve from 0.86842

Epoch 00393: val_acc did not improve from 0.86842

Epoch 00394: val_acc did not improve from 0.86842

Epoch 00395: val_acc did not improve from 0.86842

Epoch 00396: val_acc did not improve from 0.86842

Epoch 00397: val_acc did not improve from 0.86842

Epoch 00398: val_acc did not improve from 0.86842

Epoch 00399: val_acc did not improve from 0.86842

Epoch 00400: val_acc did not improve from 0.86842

Epoch 00401: val_acc did not improve from 0.86842

Epoch 00402: val_acc did not improve from 0.86842

Epoch 00403: val_acc did not improve from 0.86842

Epoch 00404: val_acc did not improve from 0.86842

Epoch 00405: val_acc did not improve from 0.86842

Epoch 00406: val_acc did not improve from 0.86842

Epoch 00407: val_acc did not improve from 0.86842

Epoch 00408: val_acc did not improve from 0.86842

Epoch 00409: val_acc did not improve from 0.86842

Epoch 00410: val_acc did not improve from 0.86842

Epoch 00411: val_acc did not improve from 0.86842

Epoch 00412: val_acc did not improve from 0.86842

Epoch 00413: val_acc did not improve from 0.86842

Epoch 00414: val_acc did not improve from 0.86842

Epoch 00415: val_acc did not improve from 0.86842

Epoch 00416: val_acc did not improve from 0.86842

Epoch 00417: val_acc did not improve from 0.86842

Epoch 00418: val_acc did not improve from 0.86842

Epoch 00419: val_acc did not improve from 0.86842

Epoch 00420: val_acc did not improve from 0.86842

Epoch 00421: val_acc did not improve from 0.86842

Epoch 00422: val_acc did not improve from 0.86842

Epoch 00423: val_acc did not improve from 0.86842

Epoch 00424: val_acc did not improve from 0.86842

Epoch 00425: val_acc did not improve from 0.86842

Epoch 00426: val_acc did not improve from 0.86842

Epoch 00427: val_acc did not improve from 0.86842

Epoch 00428: val_acc did not improve from 0.86842

Epoch 00429: val_acc did not improve from 0.86842

Epoch 00430: val_acc did not improve from 0.86842

Epoch 00431: val_acc did not improve from 0.86842

Epoch 00432: val_acc did not improve from 0.86842

Epoch 00433: val_acc did not improve from 0.86842

Epoch 00434: val_acc did not improve from 0.86842

Epoch 00435: val_acc did not improve from 0.86842

Epoch 00436: val_acc did not improve from 0.86842

Epoch 00437: val_acc did not improve from 0.86842

Epoch 00438: val_acc did not improve from 0.86842

Epoch 00439: val_acc did not improve from 0.86842

Epoch 00440: val_acc did not improve from 0.86842

Epoch 00441: val_acc did not improve from 0.86842

Epoch 00442: val_acc did not improve from 0.86842

Epoch 00443: val_acc did not improve from 0.86842

Epoch 00444: val_acc did not improve from 0.86842

Epoch 00445: val_acc did not improve from 0.86842

Epoch 00446: val_acc did not improve from 0.86842

Epoch 00447: val_acc did not improve from 0.86842

Epoch 00448: val_acc did not improve from 0.86842

Epoch 00449: val_acc did not improve from 0.86842

Epoch 00450: val_acc did not improve from 0.86842

Epoch 00451: val_acc did not improve from 0.86842

Epoch 00452: val_acc did not improve from 0.86842

Epoch 00453: val_acc did not improve from 0.86842

Epoch 00454: val_acc did not improve from 0.86842

Epoch 00455: val_acc did not improve from 0.86842

Epoch 00456: val_acc did not improve from 0.86842

Epoch 00457: val_acc did not improve from 0.86842

Epoch 00458: val_acc did not improve from 0.86842

Epoch 00459: val_acc did not improve from 0.86842

Epoch 00460: val_acc did not improve from 0.86842

Epoch 00461: val_acc did not improve from 0.86842

Epoch 00462: val_acc did not improve from 0.86842

Epoch 00463: val_acc did not improve from 0.86842

Epoch 00464: val_acc did not improve from 0.86842

Epoch 00465: val_acc did not improve from 0.86842

Epoch 00466: val_acc did not improve from 0.86842

Epoch 00467: val_acc did not improve from 0.86842

Epoch 00468: val_acc did not improve from 0.86842

Epoch 00469: val_acc did not improve from 0.86842

Epoch 00470: val_acc did not improve from 0.86842

Epoch 00471: val_acc did not improve from 0.86842

Epoch 00472: val_acc did not improve from 0.86842

Epoch 00473: val_acc did not improve from 0.86842

Epoch 00474: val_acc did not improve from 0.86842

Epoch 00475: val_acc did not improve from 0.86842

Epoch 00476: val_acc did not improve from 0.86842

Epoch 00477: val_acc did not improve from 0.86842

Epoch 00478: val_acc did not improve from 0.86842

Epoch 00479: val_acc did not improve from 0.86842

Epoch 00480: val_acc did not improve from 0.86842

Epoch 00481: val_acc did not improve from 0.86842

Epoch 00482: val_acc did not improve from 0.86842

Epoch 00483: val_acc did not improve from 0.86842

Epoch 00484: val_acc did not improve from 0.86842

Epoch 00485: val_acc did not improve from 0.86842

Epoch 00486: val_acc did not improve from 0.86842

Epoch 00487: val_acc did not improve from 0.86842

Epoch 00488: val_acc did not improve from 0.86842

Epoch 00489: val_acc did not improve from 0.86842

Epoch 00490: val_acc did not improve from 0.86842

Epoch 00491: val_acc did not improve from 0.86842

Epoch 00492: val_acc did not improve from 0.86842

Epoch 00493: val_acc did not improve from 0.86842

Epoch 00494: val_acc did not improve from 0.86842

Epoch 00495: val_acc did not improve from 0.86842

Epoch 00496: val_acc did not improve from 0.86842

Epoch 00497: val_acc did not improve from 0.86842

Epoch 00498: val_acc did not improve from 0.86842

Epoch 00499: val_acc did not improve from 0.86842

Epoch 00500: val_acc did not improve from 0.86842
CPU times: user 6min 8s, sys: 40.1 s, total: 6min 48s
Wall time: 4min 1s

4.2.2.4 Evaluate Model On Validation Set

# Define Test method


def test_valid(X_train, x_valid, y_train, y_valid, best_model, stats=False):

    # Initialize the model neural networks architecture & load weights
    model = model_neural_network_vgg()
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))

    # Evaluate on train and valid set
    print('\nCalculating Train Set accuracy ...')
    train_acc = model.evaluate(X_train, to_categorical(
        np.array([CLASS[l] for l in y_train])))
    print('Train Set accuracy: {}%'.format(
        [np.round(acc*100, 2) for acc in train_acc]))

    print('\nCalculating Valid Set accuracy ...')
    valid_acc = model.evaluate(x_valid, to_categorical(
        np.array([CLASS[l] for l in y_valid])))
    print('Valid Set accuracy: {}%'.format(
        [np.round(acc*100, 2) for acc in valid_acc]))

    if stats:
        print('\nCalculating Confusion Matrix & Classification Report ...')
        # Predictions on the valid set
        prob = model.predict(x_valid, verbose=1)
        predictions = prob.argmax(axis=1)

        # True class label
        true = np.array([CLASS[l] for l in y_valid])

        # Confusion matrix on the valid set.
        con_matrix = confusion_matrix(true, predictions)

        print('\n\nConfusion matrix of the Seedlings Valid set\n\n')
        display(pd.DataFrame(con_matrix, columns=list(
            CLASS.keys()), index=list(CLASS.keys())))

        # Plot the confusion matrix
        plot_confusion_matrix(con_matrix,
                              target_names=list(CLASS.keys()),
                              plt_name='Seedlings_Con_Matrix_VGG',
                              rootdir='./',
                              save_dir='save/con_matrix',
                              title='Confusion matrix',
                              cmap=plt.cm.Blues,
                              normalize=False)

        print('\n\nClassification Report')
        print(classification_report(true, predictions,
                                    target_names=list(CLASS.keys())))

        print('\n\nTrue vs Prediction of the Seedlings Valid set\n\n')
        predictions_df = pd.DataFrame(
            {'Image': [x for x in x_valid], 'True': true, 'Prediction': predictions})

        plot_missclassified = True
        if plot_missclassified:
            missclassified = predictions_df[predictions_df['True']
                                            != predictions_df['Prediction']]
            print('Top 10 missclassified results.')
            display(HTML(missclassified.head().to_html(index=False)))
            print('\n')
            grid_plot_for_class(missclassified.head(10))

    return valid_acc[-1]


# Testing the model on validation set

def valid_main():

    try:
        print('Loading VGG Images & Labels for validation set ...')
        x_train_botf_vgg = joblib.load(os.path.join(
            './save/pickles', 'x_train_botf_vgg.pickle'))
        x_valid_botf_vgg = joblib.load(os.path.join(
            './save/pickles', 'x_valid_botf_vgg.pickle'))
        y_train = joblib.load(os.path.join(
            './save/pickles', 'y_train_botf_vgg.pickle'))
        y_valid = joblib.load(os.path.join(
            './save/pickles', 'y_valid_botf_vgg.pickle'))
        print('Loading VGG train & valid set bottleneck features & labels done.')
    except Exception as e:
        print('Stacktrace', e)
        print('VGG Images & Labels for validation set does not exist.')

    # Best of 3 iterations
    # The best validation accuracy: 0.8621052634088616
    # Model name: ./checkpoint/VGG_ADAM_best_weights.46-0.8728.hdf5

    # The best validation accuracy: 0.8621052634088616
    # Model name: ./checkpoint/VGG_ADAM_best_weights.46-0.8728.hdf5

    # The best validation accuracy: 0.8621052634088616
    # Model name: ./checkpoint/VGG_ADAM_best_weights.46-0.8728.hdf5

    # best_model = input('Enter the name of best model saved in ./logs dir: ')
    best_model = 'VGG_ADAM_best_weights.46-0.8728.hdf5'

    run_all_saved_model = False
    # Run all the saved models & find one with best validation accuracy
    if run_all_saved_model:
        best_valid_acc = 0.0
        for saved_model in glob(os.path.join('./checkpoint', "*.hdf5")):
            if os.path.basename(saved_model).startswith('VGG_ADAM_best_weights'):
                print('*'*100)
                valid_acc = test_valid(
                    x_train_botf_vgg, x_valid_botf_vgg, y_train, y_valid, os.path.basename(saved_model))

                if (valid_acc > best_valid_acc):
                    best_valid_acc = valid_acc
                    best_model = saved_model
                    print('New best validation accuracy: {} \n Model name: {}'.format(
                        best_valid_acc, best_model))

        print('\n\nThe best validation accuracy: {} \n Model name: {}'.format(
            best_valid_acc, best_model))

    valid_acc = test_valid(x_train_botf_vgg, x_valid_botf_vgg,
                           y_train, y_valid, os.path.basename(best_model), stats=True)
    print('\n\nThe best validation accuracy: {}% \nModel name: {}'.format(
        np.round(valid_acc*100, 2), best_model))


if __name__ == '__main__':
    valid_main()

Loading VGG Images & Labels for validation set ...
Loading VGG train & valid set bottleneck features & labels done.

Calculating Train Set accuracy ...
3800/3800 [==============================] - 0s 80us/step
Train Set accuracy: [18.84, 95.95]%

Calculating Valid Set accuracy ...
950/950 [==============================] - 0s 76us/step
Valid Set accuracy: [58.72, 86.21]%

Calculating Confusion Matrix & Classification Report ...
950/950 [==============================] - 0s 209us/step


Confusion matrix of the Seedlings Valid set

	Sugar beet	Scentless Mayweed	Fat Hen	Charlock	Shepherds Purse	Loose Silky-bent	Maize	Cleavers	Black-grass	Small-flowered Cranesbill	Common wheat	Common Chickweed
Sugar beet	24	0	0	0	3	0	25	0	1	0	0	0
Scentless Mayweed	0	77	0	0	0	1	0	0	0	0	0	0
Fat Hen	0	0	53	1	0	1	0	0	0	3	0	0
Charlock	0	1	0	109	1	1	0	0	2	2	6	0
Shepherds Purse	2	0	0	0	36	1	3	0	2	0	0	0
Loose Silky-bent	1	0	1	4	2	80	0	0	2	0	0	5
Maize	10	0	0	0	0	1	117	0	3	0	0	0
Cleavers	0	0	0	0	0	0	0	43	0	0	0	1
Black-grass	0	0	0	2	0	1	4	0	94	2	0	0
Small-flowered Cranesbill	0	1	0	3	0	0	0	0	8	32	2	0
Common wheat	1	0	0	3	0	0	0	0	0	1	94	0
Common Chickweed	1	0	0	1	3	7	0	1	4	0	0	60

Saving Confusion Matrices in the ./save/con_matrix directory

Classification Report
                           precision    recall  f1-score   support

               Sugar beet       0.62      0.45      0.52        53
        Scentless Mayweed       0.97      0.99      0.98        78
                  Fat Hen       0.98      0.91      0.95        58
                 Charlock       0.89      0.89      0.89       122
          Shepherds Purse       0.80      0.82      0.81        44
         Loose Silky-bent       0.86      0.84      0.85        95
                    Maize       0.79      0.89      0.84       131
                 Cleavers       0.98      0.98      0.98        44
              Black-grass       0.81      0.91      0.86       103
Small-flowered Cranesbill       0.80      0.70      0.74        46
             Common wheat       0.92      0.95      0.94        99
         Common Chickweed       0.91      0.78      0.84        77

              avg / total       0.86      0.86      0.86       950



True vs Prediction of the Seedlings Valid set


Top 10 missclassified results.

Image	Prediction	True
[0.0, 0.0, 0.34213066, 0.0, 0.9592821, 0.0, 0....	4	11
[0.0, 0.0, 1.5533024, 0.0, 0.0016281978, 0.0, ...	6	0
[0.0, 0.0, 6.6867023, 0.14889081, 0.0, 0.0, 1....	9	3
[0.0, 0.09866158, 1.4236887, 0.053569775, 0.10...	5	3
[1.1206459, 0.0, 3.8674812, 0.19608311, 0.0608...	3	10

The best validation accuracy: 86.21% 
Model name: VGG_ADAM_best_weights.46-0.8728.hdf5

Submission on Test Set using VGG Bottleneck Features extraction

input_dim = 224
POOLING = 'avg'
RANDOM_STATE = 21

# Define Test method


def test(img, label, best_model):

    # Initialize the model neural networks architecture & load weights
    model = model_neural_network_vgg()
    model.load_weights(filepath=os.path.join('./checkpoint', best_model))

    # Predictions
    prob = model.predict(img, verbose=1)
    pred = prob.argmax(axis=-1)

    submission_df = pd.DataFrame({"file": label,
                                  "species": [INV_CLASS[p] for p in pred]})
    submission_df.to_csv(os.path.join(
        './save', 'submission_vgg.csv'), index=False, header=True)


# Testing the model

def test_main():

    # Load Seedlings Test set
    images_test_vgg = np.array([])

    try:
        print('Loading Seedlings Test set ...')
        images_test_vgg = joblib.load(os.path.join(
            './save/pickles', 'images-test-vgg.pickle'))
        ids_test_vgg = joblib.load(os.path.join(
            './save/pickles', 'ids-test-vgg.pickle'))
        print('Loading Seedlings Test set done.')
    except Exception as e:
        print('Stacktrace', e)
        print('Seedlings Test set VGG Images does not exist.')

    if (not images_test_vgg.size or not ids_test_vgg.size):
        img_size = (224, 224)
        print('Running load_seedlings method with img_size {} ...'.format(img_size))
        images_test_vgg, ids_test_vgg = load_seedlings_transfer_test(
            img_size=img_size)

        print('Dumping Seedlings Test set ...')
        # Dumping files
        joblib.dump(images_test_vgg, os.path.join(
            './save/pickles', 'images-test-vgg.pickle'))
        joblib.dump(ids_test_vgg, os.path.join(
            './save/pickles', 'ids-test-vgg.pickle'))
        print('Seedlings Test set dumping done.')

    # Type cast int to float for VGG16 model compatibility
    images_test_vgg = images_test_vgg.astype(dtype='float32', copy=False)

    print('Images Shape:', images_test_vgg.shape)
    print('Image Data Type:', images_test_vgg.dtype)
    print('Labels Shape:', ids_test_vgg.shape)
    print('Label Data Type:', ids_test_vgg.dtype)

    # VGG Preprocssing input
    # Numpy array to store extracted features from VGG model
    images_test_vgg_features = np.zeros(
        (len(images_test_vgg), input_dim, input_dim, 3), dtype='float32')

    # Extract features with VGG model
    for i, image in tqdm(enumerate(images_test_vgg)):
        images_test_vgg_features[i] = vgg16.preprocess_input(
            np.expand_dims(image, axis=0))

    print('Train Images shape: {} size: {:,}'.format(
        images_test_vgg_features.shape, images_test_vgg_features.size))

    del images_test_vgg

    # VGG Bottleneck Features extraction
    vgg_bottleneck = vgg16.VGG16(
        weights='imagenet', include_top=False, pooling=POOLING)

    # Calculating VGG train & valid set bottleneck features
    x_test_botf_vgg = vgg_bottleneck.predict(
        images_test_vgg_features, batch_size=32, verbose=1)
    joblib.dump(x_test_botf_vgg, os.path.join(
        './save/pickles', 'x_test_botf_vgg.pickle'))

    best_model = 'VGG_ADAM_best_weights.46-0.8728.hdf5'
    test(x_test_botf_vgg, ids_test_vgg, best_model)


if __name__ == '__main__':
    test_main()

Loading Seedlings Test set ...
Loading Seedlings Test set done.

152it [00:00, 1515.21it/s]

Images Shape: (794, 224, 224, 3)
Image Data Type: float32
Labels Shape: (794,)
Label Data Type: <U13

794it [00:00, 1668.95it/s]

Train Images shape: (794, 224, 224, 3) size: 119,519,232
794/794 [==============================] - 290s 365ms/step
794/794 [==============================] - 0s 67us/step

4.3 XGB

Prepare & Load Data

display(features_df.head())

	area	height	label	largest_contour_area	no_of_countours	width
0	24198.5	360	Cleavers	12418.0	3	360
1	8176.0	149	Cleavers	8176.0	1	149
2	12717.0	225	Cleavers	12717.0	1	225
3	35139.0	404	Cleavers	21444.0	5	404
4	20688.0	315	Cleavers	9436.0	12	315

# Select the numeric features, drop NA values

x_train_features =` features_df.loc[:, [
    'height', 'width', 'largest_contour_area', 'label']]
x_train_features['area_per_contour'] = features_df.loc[:,
                                                       'area'] / features_df.loc[:, 'no_of_countours']

print('Displaying Null rows/columns ...')
display(x_train_features[x_train_features.isnull().any(axis=1)])

x_train_features.dropna(inplace=True)
x_train_features.reset_index(drop=True, inplace=True)

# Pop out labels

labels = x_train_features.pop('label')

# Scale using MinMaxScaler

x_train_features_scaled = MinMaxScaler().fit_transform(x_train_features)
x_train_features.loc[:] = x_train_features_scaled

print('\nTop 10 rows')
x_train_features.head()

Displaying Null rows/columns ...

	height	width	largest_contour_area	label	area_per_contour
1769	131	131	0.0	Loose Silky-bent	NaN
2381	114	114	0.0	Common wheat	NaN
3951	91	91	0.0	Black-grass	NaN
4592	63	63	0.0	Common Chickweed	NaN

Top 10 rows

	height	width	largest_contour_area	area_per_contour
0	0.091256	0.078894	0.005999	0.016947
1	0.029343	0.025368	0.003949	0.017178
2	0.051643	0.044647	0.006143	0.026718
3	0.104167	0.090056	0.010360	0.014765
4	0.078052	0.067478	0.004558	0.003622

Encode labels using LabelEncoder

# Encode labels and create classes

le = LabelEncoder()
le.fit(labels)
labels_encoded = le.transform(labels)
print("\n\nClasses: ", le.classes_)

# Convert labels into categorical values
labels_onehot = to_categorical(labels_encoded)
print("\nNumber of One Hot encoded class labels: ", labels_onehot.shape[1])

Classes:  ['Black-grass' 'Charlock' 'Cleavers' 'Common Chickweed' 'Common wheat'
 'Fat Hen' 'Loose Silky-bent' 'Maize' 'Scentless Mayweed'
 'Shepherds Purse' 'Small-flowered Cranesbill' 'Sugar beet']

Number of One Hot encoded class labels:  12

Classifier Comparison

% % time

x_train_features = np.array(x_train_features)
labels_encoded = np.array(labels_encoded)


def classifier_comparison(n_splits=3):
    # Classifier names & initializations
    names = ["Nearest Neighbors", "SVM", "Gaussian Process",
             "Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "GradientBoost"
             "Naive Bayes", "LDA", "QDA", "XGB Classifier"]

    classifiers = [
        KNeighborsClassifier(3),
        SVC(probability=True),
        GaussianProcessClassifier(),
        DecisionTreeClassifier(),
        RandomForestClassifier(),
        MLPClassifier(),
        AdaBoostClassifier(),
        GradientBoostingClassifier(),
        GaussianNB(),
        LinearDiscriminantAnalysis(),
        QuadraticDiscriminantAnalysis(),
        XGBClassifier()]

    # Split & shuffle data using Stratified K Fold
    kfold = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=21)

    # Calculate accuracy over all splits, classifiers
    count = 0
    acc_dict = defaultdict(list)
    for train_index, test_index in kfold.split(x_train_features, labels_encoded):
        xtrain, ytrain = x_train_features[train_index], labels_encoded[train_index]
        xtest, ytest = x_train_features[test_index], labels_encoded[test_index]
        print('Iteration: ', count)

        # iterate over classifiers
        for clf in classifiers:
            clf_name = clf.__class__.__name__
            clf.fit(xtrain, ytrain)
            predictions = clf.predict(xtest)
            accuracy = accuracy_score(ytest, predictions)

            # Append results in default dict of lists
            acc_dict[clf_name].append(accuracy)

        count += 1

    return acc_dict


# Call the classifier_comparison module & save the results
acc_dict = classifier_comparison(n_splits=3)

Iteration:  0
Iteration:  1
Iteration:  2
CPU times: user 8min 42s, sys: 5min 57s, total: 14min 40s
Wall time: 4min 56s

# Dataframe containing classifier name & accuracy score
acc_df = pd.DataFrame.from_dict(acc_dict, orient='index')
acc_df = acc_df.rename(
    columns={0: '1st Iteration', 1: '2nd Iteration', 2: '3rd Iteration'})

# Calculate average accuracy
acc_df['avg_acc'] = acc_df.mean(axis=1)
acc_df = (acc_df*100).round(2)

sorted_acc_df = acc_df.sort_values(by='avg_acc', ascending=False)
display(sorted_acc_df)

	1st Iteration	2nd Iteration	3rd Iteration	avg_acc
XGBClassifier	47.64	48.45	47.78	47.96
GradientBoostingClassifier	47.76	48.64	47.21	47.87
RandomForestClassifier	44.93	44.97	45.18	45.03
KNeighborsClassifier	40.33	42.00	39.23	40.52
DecisionTreeClassifier	40.64	40.10	39.42	40.05
MLPClassifier	37.49	35.67	36.69	36.62
AdaBoostClassifier	17.64	23.66	26.68	22.66
LinearDiscriminantAnalysis	22.56	23.85	21.23	22.54
GaussianNB	20.48	23.28	21.55	21.77
GaussianProcessClassifier	20.86	20.94	19.07	20.29
QuadraticDiscriminantAnalysis	19.41	14.10	19.07	17.53
SVC	17.01	17.90	17.43	17.45

# Plot a barplot containing classifier name & accuracy score

sns.barplot(x='avg_acc', y=sorted_acc_df.index, data=sorted_acc_df)
plt.xlabel('Average Accuracy')
plt.title('Classifier Comparison based on Accuracy')

Text(0.5,1,'Classifier Comparison based on Accuracy')

It seems like simple XGB performs much better than others but accuracy is very less in comparison to a full blown CNN networks.

5. Deployment

Deployment in this context means publishing the resulting prediction from the model to the Kaggle leaderboard. To do this do the following:

1. select the cell below and run it by pressing the play button.
2. Press the Publish button in top right corner.
3. Select Output on the notebook menubar
4. Select the result dataset and press Submit to Competition button

6. Further Improvements

It's now time for you to get your hands even dirtier and go at it all by yourself in a challenge!

1. Try to use the creative data preprocessing steps, promising models, ensemble of them, different algorithms and compare their result
   • Experiment with stacking different input features in different ways (combinations) to understand their impact on the accuracy
   • Do this by optimizing hyperparameters
2. Try adding new features

The winner is the one to get the highest scoring model for the validation set.